{"as_of":"2026-08-08T12:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:86ee12e3cb70abd9f596660abf3148824781b935f19d9e79ed3e6a22c1fcbe60","coverage":[{"denominator":224,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:18:45.685359Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.05626/citation-record","integrity":"/paper/2506.05626/integrity","json":"/paper/2506.05626/citation-record.json","paper":"/paper/2506.05626"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:32.812916Z","title":"A survey on knowledge graphs: Representation, acquisition, and applications,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:32.812916Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:3fa17b40ca7cdc8ebadcb6e2e27c5336449bec0ceb2a02918a0359466629317b","observation_id":"4efdbe81-65cb-45bd-ad50-328b09e7da2d","resolution":{"observed_at":"2026-08-07T10:18:32.812916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:32.909164Z","title":"Development of knowledge graph for university courses management,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:32.909164Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:eaf708aff4a60135aeeca50ee78a8f75c980e265b32acd277f47069db5a1d974","observation_id":"d5d68962-f890-4d96-94d5-3ee6416f1a8a","resolution":{"observed_at":"2026-08-07T10:18:32.909164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:33.023190Z","title":"Knowledge graph in smart education: A case study of entrepreneurship scientific publication management,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.023190Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:1c38807b0d4018f32f5ae9d63353683fa2380672183c9939abf3bc6885fd432d","observation_id":"11810668-b724-45ac-ba7b-5f6cc73560c9","resolution":{"observed_at":"2026-08-07T10:18:33.023190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:33.140494Z","title":"Analyzing social media for measuring public attitudes toward controversies and their driving factors: a case study of migration,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.140494Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:1b9fe5ee48578e0d0a82027fa0630add459ef9d8291d4956d95ad384eb55518f","observation_id":"c2cd120a-2b20-4df0-9af9-52912fc70ee3","resolution":{"observed_at":"2026-08-07T10:18:33.140494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:33.280191Z","title":"Smr: medical knowledge graph embedding for safe medicine recommendation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.280191Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:954f0a194656fd47e04f3083475fb2fe5dffcb45c1ba19780b9d50c4ba3b98ed","observation_id":"24c9b542-ff1a-4d8e-907c-a90b1e491fdf","resolution":{"observed_at":"2026-08-07T10:18:33.280191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:33.420802Z","title":"Financial time series forecast- ing with multi-modality graph neural network,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.420802Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:792453a8281610fee6c1aa002167d4f634e1195e5a733f00418b1eb587936be8","observation_id":"d18d3d33-68b6-476c-985c-022bb42c1a7b","resolution":{"observed_at":"2026-08-07T10:18:33.420802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:33.545207Z","title":"Knowledge graphs: Opportunities and challenges,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.545207Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:e442a4676e72054705f14460f82977e1565d0db04a1d172947174abfa2f145f4","observation_id":"9487aa56-f322-4633-bcde-0b4478997813","resolution":{"observed_at":"2026-08-07T10:18:33.545207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.04572","last_updated":"2024-09-06T19:24:29Z","snapshot_observed_at":"2026-08-01T17:33:44.344130Z","submitted_at":"2024-09-06T19:24:29Z","title":"Neurosymbolic Methods for Dynamic Knowledge Graphs","version":1},"cited_work":{"arxiv_id":"2409.04572","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.04572","snapshot_observed_at":"2026-08-07T10:19:10.792058Z","title":"Neurosymbolic Methods for Dynamic Knowledge Graphs","venue":"cs.AI","work_id":"fdc5dd4b-4811-4cfe-b73b-6c2d6adf80de","year":2024},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.696373Z"},"links":{"cited_paper":"/paper/2409.04572","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:5c3c3132128fff99e92bc0c9d3865f813f5012990939ef8d10a57e65cc2d79b1","observation_id":"ec40828f-dc9c-4adf-a305-b4c7d7f7cc8d","resolution":{"observed_at":"2026-08-07T10:19:10.906202Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:33.857724Z","title":"Wordnet:: Similarity- measuring the relatedness of concepts","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.857724Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:3268cf64999ddeb95e08f3d997b12bc2ee1418e313c987b0f0b75df922122408","observation_id":"db9001da-eb76-4ba3-93d0-dedc3228aeb3","resolution":{"observed_at":"2026-08-07T10:18:33.857724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:34.033303Z","title":"Dbpedia: A nucleus for a web of open data,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.033303Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:d0112e8c9132903459969e97ae4d5816869f1e45b024da47b991bf29b3d17752","observation_id":"925b6fe0-c278-4094-8712-138eea6abefb","resolution":{"observed_at":"2026-08-07T10:18:34.033303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:34.144071Z","title":"Y AGO 4.5: A large and clean knowledge base with a rich taxonomy,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.144071Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:fa7b0829cb9d02fb30e5e0fe2232fc86a9631b353c6f128e19af9a23646e9c31","observation_id":"12aedc3d-b9fa-4497-9155-31cba1a8a6e3","resolution":{"observed_at":"2026-08-07T10:18:34.144071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:34.285170Z","title":"Free- base: a collaboratively created graph database for structuring human knowledge,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.285170Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:8bfd20a6aa9dd9978f3880cce44fd50c233b69ca0427dedf38e460163c8499b6","observation_id":"6e1c252e-6146-47b2-a164-bedbd223c26e","resolution":{"observed_at":"2026-08-07T10:18:34.285170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:34.419036Z","title":"Wikidata: a free collaborative knowl- edgebase,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.419036Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:be2e4dee59833b08bebfd7e424f2dd1cf032982d8af4a2275242ae493591a942","observation_id":"9e007725-7e92-42dd-a38c-08e7f2d4fd41","resolution":{"observed_at":"2026-08-07T10:18:34.419036Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:34.517733Z","title":"Model: Motif- based deep feature learning for link prediction,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.517733Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:629f63d5a1c34bb870c67c9e7d421dab7ee714f73c9eb78ab68b980ffdbca0d1","observation_id":"96552135-e747-41b4-ade9-0d1f03dd11d2","resolution":{"observed_at":"2026-08-07T10:18:34.517733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:34.626963Z","title":"Realistic re- evaluation of knowledge graph completion methods: An experimental study,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.626963Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:46fe9a2cd8fea3bf8eca3bb3d0726af00cba089995da6b4e6dc1d1beb10a90cc","observation_id":"c16d66bd-7d43-408c-8c76-245f8c5f36de","resolution":{"observed_at":"2026-08-07T10:18:34.626963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.06309","last_updated":"2019-06-01T03:50:14Z","snapshot_observed_at":"2026-07-06T07:02:41.032786Z","submitted_at":"2018-09-17T16:24:00Z","title":"Commonsense for Generative Multi-Hop Question Answering Tasks","version":3},"cited_work":{"arxiv_id":"1809.06309","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.06309","snapshot_observed_at":"2026-08-07T10:19:10.508402Z","title":"Commonsense for Generative Multi-Hop Question Answering Tasks","venue":"cs.CL","work_id":"08059216-c114-4354-9316-291563794a13","year":2018},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.744317Z"},"links":{"cited_paper":"/paper/1809.06309","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:68dd3f11a0e488d67b7c6bf1fc73285105e4d2a403a432563d06a0cd1dbc06b0","observation_id":"8eed0980-3c13-4bbd-98c3-d3a38bafd0f8","resolution":{"observed_at":"2026-08-07T10:19:10.610549Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:34.899749Z","title":"Predicate constraints based question answering over knowledge graph,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.899749Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:ecfd0f493ae0542182383e2e21eb19be459fc7cad72914e5c2ed4185f9a6840f","observation_id":"ce817d60-99e1-40f6-ac48-93a1c92346bb","resolution":{"observed_at":"2026-08-07T10:18:34.899749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:35.019445Z","title":"Improving multi-hop question answering over knowledge graphs using knowledge base embeddings,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.019445Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:03b715b9d7a94f608c4fa1cf6105af0ee4869838d948b7e88c2fb4a69a1d9178","observation_id":"dff0be21-6bf5-42f8-8bbc-671534877c8f","resolution":{"observed_at":"2026-08-07T10:18:35.019445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:35.133510Z","title":"A survey on knowledge-aware news recommender systems,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.133510Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:b54a357221ecf5e10024c406695aa3227ff4c08f3b13d8f6f069d671766e6d3d","observation_id":"0527a150-5908-4551-8ec1-f493e1f15531","resolution":{"observed_at":"2026-08-07T10:18:35.133510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:35.407918Z","title":"Explainable reasoning over knowledge graphs for recommendation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.407918Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:a8d45dcc56473004b124832e0865b25bf50dfea60a4942964c1edece4a626041","observation_id":"39256eb8-1a77-4b4e-8d46-7ada38021eb9","resolution":{"observed_at":"2026-08-07T10:18:35.407918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:35.526469Z","title":"Shifu2: A network representation learning based model for advisor- advisee relationship mining,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.526469Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:052f75bef8d35e314897a926e666742108b8b5c4c1831f52ab5969d4a3c480cc","observation_id":"339d2725-1006-451d-ac7c-320c2d61aa71","resolution":{"observed_at":"2026-08-07T10:18:35.526469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:35.685920Z","title":"Higher-order networks representation and learning: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.685920Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:c5f8803500dee2a6377cc8bb5be9481e9e46fbbb32d1e59d7571bc70be9020a6","observation_id":"a5ee90b0-bae7-4080-b314-0468414960e6","resolution":{"observed_at":"2026-08-07T10:18:35.685920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:35.822954Z","title":"Hplapgcn: Hypergraph p-laplacian graph convolutional networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.822954Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:184a69700c03488edb2d390661a60428e7de28abb8e66a7755eec12574f9fafd","observation_id":"16526aa8-ea6d-4330-bfb4-5d3babb51138","resolution":{"observed_at":"2026-08-07T10:18:35.822954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:35.990643Z","title":"Nonuniform hyper- network embedding with dual mechanism,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.990643Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:b1ecc85a4592d46645146e95de1bc3e564d5dc9aeb0003574d03833e83a4821e","observation_id":"a9ea0466-d860-439e-a5aa-e194b0807246","resolution":{"observed_at":"2026-08-07T10:18:35.990643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:36.123337Z","title":"Dynamic hypergraph neural networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:36.123337Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:6c8a8d5be5adda19a8f4c86f50f1f83012feaf3300b42acf45e1ffab5887a606","observation_id":"65d733ed-2b3b-417f-bd9f-af371bd7357a","resolution":{"observed_at":"2026-08-07T10:18:36.123337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.02633","last_updated":"2019-10-07T06:56:02Z","snapshot_observed_at":"2026-08-03T12:05:27.972764Z","submitted_at":"2019-10-07T06:56:02Z","title":"Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs","version":1},"cited_work":{"arxiv_id":"1910.02633","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.02633","snapshot_observed_at":"2026-08-07T10:19:10.277978Z","title":"Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs","venue":"cs.LG","work_id":"7472b81f-2dad-43fe-84fb-26e9533ddcfe","year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:36.280075Z"},"links":{"cited_paper":"/paper/1910.02633","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:7239f6de51e5c802a2456ee6a370ffb2874c2f335f620e4d0f47045b7afe2d3f","observation_id":"b52c407a-f560-48a8-8937-2d325ac33263","resolution":{"observed_at":"2026-08-07T10:19:10.371083Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:36.727090Z","title":"Netvec: A scalable hypergraph embedding system,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:36.727090Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:c84216f083a13b9ee823fe9310de47c95912b516f6500d0aa9c620348e285e77","observation_id":"33590f93-650e-4839-b800-352cb95ed6da","resolution":{"observed_at":"2026-08-07T10:18:36.727090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:36.839863Z","title":"Practical parallel hypergraph algorithms,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:36.839863Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:6b9a39ccc5277faa6df89291d39c09cb321c28e215bfb733b6e8c33b60b1d929","observation_id":"4adce25c-ce48-46ea-b17b-7b8be49f63c5","resolution":{"observed_at":"2026-08-07T10:18:36.839863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:36.977510Z","title":"Learning over families of sets-hypergraph representation learning for higher order tasks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:36.977510Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:9a82b3660e4377faefed56cd2b6968018fd6cf1424d0d2900fd582a1a1d027a6","observation_id":"b18e377a-0d76-46c4-acf6-6047851e93a4","resolution":{"observed_at":"2026-08-07T10:18:36.977510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:37.036189Z","title":"Modeling multi-way relations with hypergraph embedding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:37.036189Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:4d86bcb97527ba1ac447b6625ee948e776ec36b6e8b2bafd0dbf4d7722756477","observation_id":"2ab44778-361f-46c8-96f6-9a7b72798f3f","resolution":{"observed_at":"2026-08-07T10:18:37.036189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.00137","last_updated":"2020-07-15T13:39:31Z","snapshot_observed_at":"2026-07-06T07:57:10.401964Z","submitted_at":"2019-06-01T03:03:15Z","title":"Knowledge Hypergraphs: Prediction Beyond Binary Relations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.00137","snapshot_observed_at":"2026-08-07T10:18:37.185203Z","title":"Knowledge hypergraphs: Prediction beyond binary relations,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:37.185203Z"},"links":{"cited_paper":"/paper/1906.00137","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:9d1ce55bac8948abe38bd47171564290c59d2ffad8b2493ced4d10abc4dc843c","observation_id":"8a2b7099-b98f-4344-b9b9-bc55fd1f6c2f","resolution":{"observed_at":"2026-08-07T10:18:37.185203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:37.339539Z","title":"Neuinfer: Knowledge inference on n-ary facts,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:37.339539Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:69ddf7bc73da4cac17c920a6a33e267127262644c088cda1c751b61d46fec006","observation_id":"6091b344-de22-4589-930a-068a4d31a6e8","resolution":{"observed_at":"2026-08-07T10:18:37.339539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04782","last_updated":"2024-03-02T16:21:45Z","snapshot_observed_at":"2026-07-06T17:41:10.677334Z","submitted_at":"2024-03-02T16:21:45Z","title":"A Survey on Temporal Knowledge Graph: Representation Learning and Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04782","snapshot_observed_at":"2026-08-07T10:18:37.461996Z","title":"A survey on temporal knowledge graph: Representation learning and applications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:37.461996Z"},"links":{"cited_paper":"/paper/2403.04782","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:77097b293051e7a0c785e935ec9966ce7aa697b19509ef3530835cc3be4ff295","observation_id":"182caa62-576f-4794-af76-2b2f1253b560","resolution":{"observed_at":"2026-08-07T10:18:37.461996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:37.661692Z","title":"Yago2: exploring and querying world knowledge in time, space, context, and many languages,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:37.661692Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:41e8487c5ee21c3d08601cd43843ccdda03374abf4e307dfb4ad88aaa0432fee","observation_id":"c59fd93b-7e9b-4742-aff5-1a2776639cdf","resolution":{"observed_at":"2026-08-07T10:18:37.661692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1604.08642","last_updated":"2016-04-28T22:42:38Z","snapshot_observed_at":"2026-08-04T04:24:16.193259Z","submitted_at":"2016-04-28T22:42:38Z","title":"On the representation and embedding of knowledge bases beyond binary relations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.08642","snapshot_observed_at":"2026-08-07T10:18:37.830122Z","title":"On the representation and embedding of knowledge bases beyond binary relations,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:37.830122Z"},"links":{"cited_paper":"/paper/1604.08642","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:d02fab33dc7a872ec7f7a7492f7c2d191dc396734abbdb49e94b9e4c2c77b861","observation_id":"0cea0939-8549-419a-8678-421e5b456637","resolution":{"observed_at":"2026-08-07T10:18:37.830122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:37.994814Z","title":"Neural message passing for multi-relational ordered and recursive hypergraphs,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:37.994814Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:dd4082db7bf5233470209ba33c766f857c37023ff9ef742c2d7b5df88c906a91","observation_id":"d3a998f6-c352-484f-97de-74696807779f","resolution":{"observed_at":"2026-08-07T10:18:37.994814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12158","last_updated":"2024-12-11T12:03:33Z","snapshot_observed_at":"2026-08-05T15:41:40.713631Z","submitted_at":"2024-12-11T12:03:33Z","title":"Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12158","snapshot_observed_at":"2026-08-07T10:18:38.186662Z","title":"Hyperbolic hypergraph neural networks for multi-relational knowledge hypergraph representa- tion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:38.186662Z"},"links":{"cited_paper":"/paper/2412.12158","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:30665fcca5b7db389aa271861e8d7ba6cb987fa16c93c50407119f767f7dc5ba","observation_id":"74296641-bd18-418e-a903-52deda8e7501","resolution":{"observed_at":"2026-08-07T10:18:38.186662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:38.361885Z","title":"A survey on hypergraph representation learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:38.361885Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:64a831c6edf6420c573bffeb2069b02e2235a01462ec50019a2095bdf67ef3ec","observation_id":"fc71dd25-e4b1-414d-ae1e-a63a01c6a6b2","resolution":{"observed_at":"2026-08-07T10:18:38.361885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:38.473457Z","title":"Poskhg: A position-aware knowledge hypergraph model for link prediction,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:38.473457Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:fa0ded472aa637ee1ab6991ef76f21053edd466340e96b31d14f369f1022bb3e","observation_id":"7fb5b08a-6ef5-4ab4-8f55-23e59f9925f6","resolution":{"observed_at":"2026-08-07T10:18:38.473457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.10847","last_updated":"2020-09-22T22:38:54Z","snapshot_observed_at":"2026-07-06T09:57:57.267506Z","submitted_at":"2020-09-22T22:38:54Z","title":"Message Passing for Hyper-Relational Knowledge Graphs","version":1},"cited_work":{"arxiv_id":"2009.10847","doi":null,"metadata_source":"pith","pith_arxiv_id":"2009.10847","snapshot_observed_at":"2026-08-07T10:19:10.018437Z","title":"Message Passing for Hyper-Relational Knowledge Graphs","venue":"cs.LG","work_id":"c5d2c7c3-0fad-4bb5-9207-0705532540a6","year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:38.578650Z"},"links":{"cited_paper":"/paper/2009.10847","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:ab1ca59c7def4436fbf0fa234e71d09a96793a0fe602ae2891bbbf7eedd956b3","observation_id":"805b9886-cf43-4b48-b939-505e9e718322","resolution":{"observed_at":"2026-08-07T10:19:10.133375Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:38.754789Z","title":"Temporal knowledge graph reasoning based on n-tuple modeling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:38.754789Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:7c4bf2fd8e2d5a7091d395c75bb90e7f4bdc687ec2756391ac4d9d183b988c2b","observation_id":"70c15601-dbca-4355-9fab-a0b23b5c4702","resolution":{"observed_at":"2026-08-07T10:18:38.754789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.10219","last_updated":"2024-10-03T21:43:43Z","snapshot_observed_at":"2026-08-05T23:02:04.977929Z","submitted_at":"2023-07-14T21:29:16Z","title":"Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.10219","snapshot_observed_at":"2026-08-07T10:18:38.951377Z","title":"Temporal fact reasoning over hyper-relational knowledge graphs,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:38.951377Z"},"links":{"cited_paper":"/paper/2307.10219","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:41dfdc52093db9a6ae0a8ec86daf1f48b7c3fbedfd6b4aaf5b6d6e992b573888","observation_id":"37eb8b38-a619-412d-b93a-033ba1c9ec4e","resolution":{"observed_at":"2026-08-07T10:18:38.951377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:39.096552Z","title":"Learning represen- tations for hyper-relational knowledge graphs,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:39.096552Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:77f1ac1705b5686b8d00708ba430e6e4044de0fcdb928c76ab15b69388701c6e","observation_id":"d22f90ce-d2a7-4006-a190-c9ce667d4101","resolution":{"observed_at":"2026-08-07T10:18:39.096552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.08476","last_updated":"2021-05-18T12:40:35Z","snapshot_observed_at":"2026-08-07T03:32:43.184124Z","submitted_at":"2021-05-18T12:40:35Z","title":"Link Prediction on N-ary Relational Facts: A Graph-based Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.08476","snapshot_observed_at":"2026-08-07T10:18:39.234249Z","title":"Link prediction on n-ary relational facts: A graph-based approach,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:39.234249Z"},"links":{"cited_paper":"/paper/2105.08476","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:baa3a37054d19d1fbb55f812a8f9b098f87703cf0786dc2980d1e4293398bd36","observation_id":"2c83e428-9447-419f-b4c1-aacec83b5946","resolution":{"observed_at":"2026-08-07T10:18:39.234249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:39.342751Z","title":"Generalizing tensor decomposition for n- ary relational knowledge bases,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:39.342751Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:86033b88cf1cc7a0eea3286326e8df14e3fe848b48f9ddd53124d810135bbc01","observation_id":"94c58137-df61-4d13-81fb-72f54438bb67","resolution":{"observed_at":"2026-08-07T10:18:39.342751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:39.433834Z","title":"From knowledge graph embedding to ontology embedding? an analysis of the compatibility between vector space representations and rules,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:39.433834Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:171ecbd590ace6c9fee812ca4b0e3d6ac37e75fbbb2a951433880f7f076b7874","observation_id":"c96f88dc-820d-480c-877e-317542f4872d","resolution":{"observed_at":"2026-08-07T10:18:39.433834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:39.674451Z","title":"Knowledge graph embed- ding: A survey from the perspective of representation spaces,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:39.674451Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:3ca513648d7073843b47a589be9204c8413da2beab617ca66b1c8438e72551cf","observation_id":"ab70d0fb-3634-4221-acc2-8bf7f2d4d5f3","resolution":{"observed_at":"2026-08-07T10:18:39.674451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:39.798233Z","title":"Hypergraph learning: Methods and practices,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:39.798233Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:5f1e5c001d2d1cd0ef8d92370965bf76345408fdb47c8d59715c7313118782d3","observation_id":"5ba356e7-590b-43d0-a8ab-72fd887e4f9d","resolution":{"observed_at":"2026-08-07T10:18:39.798233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:39.880194Z","title":"Hypergraph and uncertain hypergraph representation learning theory and methods,","venue":null,"work_id":null,"year":1921},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:39.880194Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:ab98a3b6fa89fc61283db907b2bfd062c6bdcc005392416960582db4e542355b","observation_id":"90b6e194-8a1c-407f-b9cd-d1c0ff4f271f","resolution":{"observed_at":"2026-08-07T10:18:39.880194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:39.976853Z","title":"Hyper-path-based representation learn- ing for hyper-networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:39.976853Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:101e081d0109f9d895cb6f1edea88a9276763a995e4e5e7d98be8fc5d28d507c","observation_id":"3507ecc2-3f90-4e6a-89a2-5edb1788ba9a","resolution":{"observed_at":"2026-08-07T10:18:39.976853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.105143Z","title":"Lbsn2vec++: Het- erogeneous hypergraph embedding for location-based social networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.105143Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:9ab6a0f6e17fbff0b0756fa046ed848262cd3b2f94e9e94197ee47bc6cb276a0","observation_id":"4b7f9aac-6e7d-4981-ace9-d4aa546393bf","resolution":{"observed_at":"2026-08-07T10:18:40.105143Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.237920Z","title":"Exploiting cross-order patterns and link prediction in higher-order networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.237920Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:4915a957ca98709eac4e79c6563a6d1be57d3f8e42c43c23288b99ce0d036ed1","observation_id":"427c6fed-cc5d-443b-9ed1-b1f84987ed4a","resolution":{"observed_at":"2026-08-07T10:18:40.237920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.294319Z","title":"Hypergraph theory,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.294319Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:f00856a747dca8d6df7e7d209805a9fa3bec75b1ccf44b546721cd4ff99f4812","observation_id":"40a5397c-2f35-4064-84fd-ad8f0ffcca3b","resolution":{"observed_at":"2026-08-07T10:18:40.294319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.385203Z","title":"Laplacian eigenmaps for dimensionality reduction and data representation,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.385203Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:be258654764b482cea75a1eb01bf1649de77e99fb8fbf58c06c867b0f3811a28","observation_id":"e05510fa-eb17-452c-99d2-c4cf4ba15631","resolution":{"observed_at":"2026-08-07T10:18:40.385203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.494224Z","title":"A unified feature selection framework for graph embedding on high dimensional data,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.494224Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:468c17d9521a9d81e33563a360e2d00db9b7f76cbdd632c1d59127a01eb4ec12","observation_id":"8a8fb8bf-2940-4ed9-9208-52dfd2387a54","resolution":{"observed_at":"2026-08-07T10:18:40.494224Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.531776Z","title":"Laplacian score for feature selection,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.531776Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:0a57b86249812e3dbd3f126430340c516f4f861d40c046a95de36f9e84d6c78b","observation_id":"493ebc5e-05df-4ab3-86b6-b20b0c0691c4","resolution":{"observed_at":"2026-08-07T10:18:40.531776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.620765Z","title":"Learning with hypergraphs: Clustering, classification, and embedding,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.620765Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:94b70eaa4229e398ab2959c1b62add1f97b3f3246179de875e34f335919fc09e","observation_id":"0396005d-9a60-4d3e-b0bb-3fb6b614a316","resolution":{"observed_at":"2026-08-07T10:18:40.620765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.696510Z","title":"Spectra, euclidean representations and clusterings of hyper- graphs,","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.696510Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:c1a2d4b156a54fc16b32e57f928aa7b9dca1ea7968e4a36ce777eb9b70823ad1","observation_id":"39e4c9be-1b8c-4e3e-9363-4cb7fb7c08e8","resolution":{"observed_at":"2026-08-07T10:18:40.696510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.765783Z","title":"Hypergraph learning with hyperedge ex- pansion,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.765783Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:656c5aa6bb6f895c41cc08be1398f66fc21ee2fb86df8fc2d9828fc1aa261099","observation_id":"a3de74ec-3822-4b60-9857-12aeddabae29","resolution":{"observed_at":"2026-08-07T10:18:40.765783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.859327Z","title":"Heterogeneous hypergraph embedding for document recommendation,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.859327Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:7710d06bf46c0ba447b858e911bd72170f314a4359af18fbe3ec8f66e5915280","observation_id":"d9ed6912-0c8d-4562-9019-07375c1377ff","resolution":{"observed_at":"2026-08-07T10:18:40.859327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.920777Z","title":"On the laplacian eigenvalues and metric parameters of hypergraphs,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.920777Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:66dcb3a821ce34b9c62e97e33e86351dbde08ba2ad1081d068419077928c51f3","observation_id":"b525644b-0bf6-4832-8bb6-b9e6b7d1270e","resolution":{"observed_at":"2026-08-07T10:18:40.920777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:40.996221Z","title":"Hypergraph p-laplacian: A differential geometry view,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:40.996221Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:0c0c363c3f6eb1116e1c254c0b37c264f78017859b3ae89cd506e857e87d9959","observation_id":"322c5a9d-467a-4aed-a8ae-e4f0a17964ef","resolution":{"observed_at":"2026-08-07T10:18:40.996221Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:41.122959Z","title":"Hypergraph embedding for spatial-spectral joint feature extraction in hyperspectral images,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:41.122959Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:8ba5ad85b4b8a3656c485013b8857a687abc8902c44101e29ed7b125d5b8f965","observation_id":"58886f9e-1593-4ea2-9e88-88add481ed3a","resolution":{"observed_at":"2026-08-07T10:18:41.122959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:41.221425Z","title":"Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral im- age,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:41.221425Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:c464232e2d2331db95302cae30f359c61ba34aebd5b57653890bd38bf92de36e","observation_id":"7e2e0da7-8c98-4855-b020-f6e79f8f8571","resolution":{"observed_at":"2026-08-07T10:18:41.221425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:41.366610Z","title":"Semisupervised hypergraph discriminant learning for dimensionality reduction of hy- perspectral image,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:41.366610Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:12ae907aa8cafbea95e004493bfd8aac926d1180350dec68d2d29b0d144b502b","observation_id":"67fbe3d0-85e8-4e24-8ab3-fd6d0d0ba735","resolution":{"observed_at":"2026-08-07T10:18:41.366610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:41.468628Z","title":"Learning with hypergraph for hyperspec- tral image feature extraction,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:41.468628Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:5d68d6e9ad720b43b10599440b70d92b8ebe07afcdf24477aeac38f657e94e97","observation_id":"be8ad3a9-64cf-4544-90ec-0769250ec3ae","resolution":{"observed_at":"2026-08-07T10:18:41.468628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:41.581373Z","title":"Discriminant hyper-laplacian projections and its scalable extension for dimensionality reduction,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:41.581373Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:5d755f2f42bc3cd48cc88c7e7c297da8761e8da12ddc0e6750d87a668e8da7d8","observation_id":"cff2d15a-e8f9-4339-8b40-1045bbd19d4f","resolution":{"observed_at":"2026-08-07T10:18:41.581373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:41.734846Z","title":"Em- bedding learning with events in heterogeneous information networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:41.734846Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:25c07b86b8d13f28f7cc5d2a0bb9ef268bbc0da63c40155a79cc8bf5d5554dc1","observation_id":"3d24129a-4c79-4f19-aaca-76643beef551","resolution":{"observed_at":"2026-08-07T10:18:41.734846Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:41.814819Z","title":"Large-scale embedding learning in heterogeneous event data,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:41.814819Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:bc9bba93941496458147b43a8f9701992724a439edd7f51072e4ccd7d018ce39","observation_id":"77507b3a-21eb-4cbe-8d74-3b3e99d47528","resolution":{"observed_at":"2026-08-07T10:18:41.814819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:41.932996Z","title":"Hypergraph partitioning with embeddings,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:41.932996Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:b75ba4c640ac3dcb3d936f528c1acc0223af7b28065e6cfbdb5573e19fd47737","observation_id":"94a02fef-6ffb-4e4b-9cf3-7ac78c8a73b0","resolution":{"observed_at":"2026-08-07T10:18:41.932996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:42.059542Z","title":"Revisiting user mobility and social relationships in lbsns: a hypergraph embedding approach,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:42.059542Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:517ea6ba62d6674f7d5020056e9aa1a1d83650065e62db12a6f5b9184749922d","observation_id":"797e48fb-7b12-464b-85e2-4b388c54c14c","resolution":{"observed_at":"2026-08-07T10:18:42.059542Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:42.151144Z","title":"Learning holistic interactions in lbsns with high-order, dynamic, and multi-role contexts,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:42.151144Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:3ac8f6074c856fbcf4ee1a14b4bb180ca0703f3d4331d9ce1139a2432036b92c","observation_id":"134ca3f3-34a8-4392-9ff6-c0b8032e3dbe","resolution":{"observed_at":"2026-08-07T10:18:42.151144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3781","last_updated":"2013-09-07T00:30:40Z","snapshot_observed_at":"2026-07-06T03:04:11.148340Z","submitted_at":"2013-01-16T18:24:43Z","title":"Efficient Estimation of Word Representations in Vector Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3781","snapshot_observed_at":"2026-08-07T10:18:42.260703Z","title":"Efficient estimation of word representations in vector space,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:42.260703Z"},"links":{"cited_paper":"/paper/1301.3781","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:2a8de98f9ade7e98071093f62a6bedb81fe0bd221562bfbb48efab12a304c23c","observation_id":"e8c7cd36-0d78-45de-aa48-64cd28344aa9","resolution":{"observed_at":"2026-08-07T10:18:42.260703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:42.357277Z","title":"Hyper2vec: Biased random walk for hyper-network embedding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:42.357277Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:cef3057faad6ea51e1b4a2257bd2025416063623ae77ea18debd5e15298c0e0d","observation_id":"8037b59e-5160-4cd3-92ed-e48cd13a53f1","resolution":{"observed_at":"2026-08-07T10:18:42.357277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:42.449957Z","title":"Social-guided representation learning for images via deep heterogeneous hypergraph embedding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:42.449957Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:7d4ba2e0a0f08978d5b7e62496bd57757c57e0298bbafd4250692443aa50f361","observation_id":"f0bfa729-22b9-4061-9db7-bd3b7311a4e9","resolution":{"observed_at":"2026-08-07T10:18:42.449957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:42.545173Z","title":"Music recommendation via hypergraph embedding,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:42.545173Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:4b68af2b07e61976f3a23145150fd567e3f29b67313f6ac1bf397b9480c50f8c","observation_id":"922b9012-c16c-4ed9-883b-6be68d53b87a","resolution":{"observed_at":"2026-08-07T10:18:42.545173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.02613","last_updated":"2019-11-06T20:10:24Z","snapshot_observed_at":"2026-07-06T08:35:15.491391Z","submitted_at":"2019-11-06T20:10:24Z","title":"Hyper-SAGNN: a self-attention based graph neural network for hypergraphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.02613","snapshot_observed_at":"2026-08-07T10:18:42.647393Z","title":"Hyper-sagnn: a self-attention based graph neural network for hypergraphs,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:42.647393Z"},"links":{"cited_paper":"/paper/1911.02613","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:ca908e0aff39b45a15c71ab9f42e876e0897970c97e4f501a32603be10906517","observation_id":"6aff9f8c-a7aa-4ec1-ae30-1e2d993398b4","resolution":{"observed_at":"2026-08-07T10:18:42.647393Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:42.748905Z","title":"Hypernetwork representation learning with the set constraint,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:42.748905Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:f96d2aee2b10a17739e2ee9720838e9b0f3cdbc9dbcf681830791050e2569ecc","observation_id":"cb8af8cd-709f-4178-a426-7773cb5e3b50","resolution":{"observed_at":"2026-08-07T10:18:42.748905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:42.926308Z","title":"Deepwalk: Online learning of social representations,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:42.926308Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:f27017811974bdf1ffbd9de401f9da0ab8175978bf4499fd67f7437426563748","observation_id":"59888018-bcb0-4fb8-a701-2571ef0e67b3","resolution":{"observed_at":"2026-08-07T10:18:42.926308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:43.062769Z","title":"Line: Large-scale information network embedding,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:43.062769Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:02a1650b1b878b7d395b0ad246ffd744322d14bedd2a87505c4d528b3dc34b04","observation_id":"5a37275c-b538-40d9-99c5-f03905ad40fc","resolution":{"observed_at":"2026-08-07T10:18:43.062769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:43.179646Z","title":"node2vec: Scalable feature learning for networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:43.179646Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:9519758108f318d082d196fbe76dad991e3c36b0f37e617e9af25caf46c8e013","observation_id":"5bad7ed2-44d5-4ddd-ac87-b03318ac56f2","resolution":{"observed_at":"2026-08-07T10:18:43.179646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:43.335643Z","title":"Hypergraph neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:43.335643Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:646d1c967be5db31036934c2767e95df3a4982dba9bb1af0acf0512c289f08c1","observation_id":"b55cc845-44a4-40e7-8019-9e060983f495","resolution":{"observed_at":"2026-08-07T10:18:43.335643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.02589","last_updated":"2019-05-22T13:48:41Z","snapshot_observed_at":"2026-07-06T06:59:52.208971Z","submitted_at":"2018-09-07T17:27:25Z","title":"HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs","version":4},"cited_work":{"arxiv_id":"1809.02589","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.02589","snapshot_observed_at":"2026-08-07T10:19:09.779824Z","title":"HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs","venue":"cs.LG","work_id":"da6982d3-1e4f-4de3-9e3a-8047ece35425","year":2018},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:43.470558Z"},"links":{"cited_paper":"/paper/1809.02589","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:4028c35c8289c2a674688f3ea39b01b1117de51c3d5cca0c817fce1e2866822c","observation_id":"0ca4d192-cc5d-43c2-9228-c0b640c3f9b5","resolution":{"observed_at":"2026-08-07T10:19:09.846316Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:43.596733Z","title":"Heterogeneous hypergraph embedding for graph classification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:43.596733Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:b46a48a32f152e37757aae1788b2287ed3e5eed6b09ecd59e3e60a3c66db2bd9","observation_id":"11cedf0d-6ac2-42e6-9069-09656b1bd345","resolution":{"observed_at":"2026-08-07T10:18:43.596733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:43.725577Z","title":"Adahgnn: Adaptive hypergraph neural networks for multi-label image classification,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:43.725577Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:da9dcb9f018bcf3819a59c8b9fc3bd3ec8fb0085b8454431f13a56c321e11672","observation_id":"fbfa0a52-675f-4d3a-91ce-01a71ea4f4dc","resolution":{"observed_at":"2026-08-07T10:18:43.725577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.00956","last_updated":"2021-05-03T15:48:34Z","snapshot_observed_at":"2026-08-07T20:11:41.115546Z","submitted_at":"2021-05-03T15:48:34Z","title":"UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.00956","snapshot_observed_at":"2026-08-07T10:18:43.858739Z","title":"Unignn: a unified framework for graph and hypergraph neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:43.858739Z"},"links":{"cited_paper":"/paper/2105.00956","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:ccff512a397627b27a6d3f9f3b2175abb836bd1ef58a9f23f6c8c23b0229d3c1","observation_id":"9e61fb15-b699-4527-9c63-7af5faa0d6a9","resolution":{"observed_at":"2026-08-07T10:18:43.858739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:44.008459Z","title":"Joint personalized search and recommendation with hypergraph convolutional networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:44.008459Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:f523b8b3893df2303149ba16fa2712445362c6b97f1060ed7774ed061f7b05e7","observation_id":"4ee1ec50-38f6-4aac-a815-53f1272f165b","resolution":{"observed_at":"2026-08-07T10:18:44.008459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.12278","last_updated":"2020-06-22T14:08:32Z","snapshot_observed_at":"2026-08-08T06:09:19.630921Z","submitted_at":"2020-06-22T14:08:32Z","title":"HNHN: Hypergraph Networks with Hyperedge Neurons","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.12278","snapshot_observed_at":"2026-08-07T10:18:44.080260Z","title":"Hnhn: Hypergraph networks with hyperedge neurons,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:44.080260Z"},"links":{"cited_paper":"/paper/2006.12278","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:4c17d7f506ad37c3896ee3e528eb2648b56259b65279dbde1a91e64070f5a27a","observation_id":"f640a2a4-eead-4b87-b281-5113fe1cd947","resolution":{"observed_at":"2026-08-07T10:18:44.080260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:44.219410Z","title":"Hypergraph convolutional recurrent neural network,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:44.219410Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:16659fe2d319ed106da846dc77fc4ff5e9bcf44570e01a124ab0d1ca914dfe1b","observation_id":"0c3a10e2-306d-45d7-a779-2781a0253d0f","resolution":{"observed_at":"2026-08-07T10:18:44.219410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:44.342233Z","title":"Edge repre- sentation learning with hypergraphs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:44.342233Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:7c358850fd990cdf9a794a8f12607bb061ac95c6d53a5dad847f9fd3e8834808","observation_id":"1b34c537-8e9e-4a32-97dd-bfe4b1448bca","resolution":{"observed_at":"2026-08-07T10:18:44.342233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:44.506985Z","title":"Knowledge-aware hypergraph neural network for recommender sys- tems,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:44.506985Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:3da220a7c41003ae9e87760bd04614cc1fecd7fa54710aae31c562a3496ba210","observation_id":"b7c4febb-8017-46b4-9c02-7be29125f216","resolution":{"observed_at":"2026-08-07T10:18:44.506985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:44.635841Z","title":"Self-supervised hypergraph convolutional networks for session-based recommenda- tion,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:44.635841Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:5ecef125cd019326e326d096df94bff90e785fa34fec394aea07d1705dc913cc","observation_id":"9784a179-1e5f-4a53-8a6d-9e797a7325b5","resolution":{"observed_at":"2026-08-07T10:18:44.635841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:44.769869Z","title":"Self- supervised multi-channel hypergraph convolutional network for social recommendation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:44.769869Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:863097ed629a65612d2b42901078a40829ff03073b43992194591edd9ee1ff1a","observation_id":"22b9f5fa-e0ea-4229-83da-306847009afa","resolution":{"observed_at":"2026-08-07T10:18:44.769869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:44.920400Z","title":"Heterogeneous graph attention network,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:44.920400Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:d882b6f4caf29ca9b14ff04cf15e1935eb4c1dc374bb42883ea8d7527326e9b9","observation_id":"93a2b463-f45f-41b2-a1b2-c1b44eaffd04","resolution":{"observed_at":"2026-08-07T10:18:44.920400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.00387","last_updated":"2020-11-01T00:21:59Z","snapshot_observed_at":"2026-07-06T10:10:33.121488Z","submitted_at":"2020-11-01T00:21:59Z","title":"Be More with Less: Hypergraph Attention Networks for Inductive Text Classification","version":1},"cited_work":{"arxiv_id":"2011.00387","doi":null,"metadata_source":"pith","pith_arxiv_id":"2011.00387","snapshot_observed_at":"2026-08-07T10:19:09.570649Z","title":"Be More with Less: Hypergraph Attention Networks for Inductive Text Classification","venue":"cs.CL","work_id":"f03c0b64-4f9f-40f2-95c4-9a3254c7723d","year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:45.084913Z"},"links":{"cited_paper":"/paper/2011.00387","citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:6b74edb2711ee591cce80e4475a3545acc35b609f6b798ee1e53ee11bb2a90d7","observation_id":"4a4c6727-12c2-444c-b447-85b624009810","resolution":{"observed_at":"2026-08-07T10:19:09.656020Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:45.239285Z","title":"Session-based recommen- dation with hypergraph attention networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:45.239285Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:45fb90d39d0afc1f57836ee7be55689103acb6c287a136f5b7df501d9e5b4a20","observation_id":"f82c7a0c-c5c7-4ad0-be01-50fb2b3acd16","resolution":{"observed_at":"2026-08-07T10:18:45.239285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:45.373546Z","title":"Hypergraph neural networks with attention mechanism for session-based recommendation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:45.373546Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:1c4cf8ce0284b4982a03b5a8f975e315a9d77660cf44cfcfbe545e9c0f8d86fa","observation_id":"1c03ad88-968d-40fb-bdfb-dc2fdcf22cf9","resolution":{"observed_at":"2026-08-07T10:18:45.373546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:45.480795Z","title":"Hypergraph attention networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:45.480795Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:6cd2d2d92dc6ceacf308375cd1b905704b2b49567f93c8ed745dd35821685e4a","observation_id":"db8238dd-6d0c-45c9-98cc-467cc7e5c5ec","resolution":{"observed_at":"2026-08-07T10:18:45.480795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:45.574824Z","title":"Dh-hgcn: dual homogeneity hy- pergraph convolutional network for multiple social recommendations,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:45.574824Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:40cb1b0ee61bc40cc38faad657aacc8c9a7326cb3691789a65688b0f23e1e13e","observation_id":"f9654797-f387-468d-8775-781e1c8cf8f9","resolution":{"observed_at":"2026-08-07T10:18:45.574824Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:45.685359Z","title":"Directed hypergraph attention network for traffic forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods","version":2},"reference_index":103,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:45.685359Z"},"links":{"citing_paper":"/paper/2506.05626"},"observation_digest":"sha256:35284c2dd2cfa8a827fa4932a0c9facd22d38aada047824336ebd3cf7daeaeab","observation_id":"2c5e47b5-9f8b-4581-bddc-f7b29b1e2c4f","resolution":{"observed_at":"2026-08-07T10:18:45.685359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.05626","last_updated":"2025-06-29T19:40:42Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T03:19:25.880206Z","submitted_at":"2025-06-05T22:59:39Z","title":"Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":94,"verified_exact":6,"verified_fuzzy":0},"total_outbound_references":224},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 100 of 224 outbound references and 0 inbound Pith citation observations for arXiv:2506.05626."}