{"as_of":"2026-08-06T08:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7771dd334d5dc531d4ee2c7911edc25c6155c48ae86a47dc4f275d00b05ad101","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T02:26:06.984348Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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/2606.05639/citation-record","integrity":"/paper/2606.05639/integrity","json":"/paper/2606.05639/citation-record.json","paper":"/paper/2606.05639"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T02:26:06.984348Z","title":"Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:88ce476efc5b6089a25711372a53760e6c6bff693079ca11eea6681421ed7279","observation_id":"d979a86f-2dde-40b2-a257-40109b9e6b9f","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Knowledge enhanced representation learning for drug discovery","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:b0499414e2ec1249cdb670093d80302266562110b30ecd08902f2043300736c3","observation_id":"4e2e79b5-0fd7-40e7-8fa6-7ec4e19a6fea","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Reckg: Knowledge graph for recommender systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:8020a2920af28a3b5a1ff5714390189c90a9cbf43fa807e2ff54df9c7ded422c","observation_id":"4e4dd197-88e5-4fe6-8c0c-b2d9b56d3ded","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Bridging the user-side knowledge gap in knowledge-aware recommendations with large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:48179deaad17e39c7d045ccfcfbf0d35737a06a773e521513a60e04c299f25ec","observation_id":"92952cc4-53ad-4053-9791-a86af1357e17","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"D-RAG: Differentiable retrieval-augmented generation for knowledge graph question answering","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:6f54d1c89d54a4cd2db6b95915fc5965b7b60f2d6501a218cfce8540074d5fc5","observation_id":"caa86798-beb4-4f83-876c-4feae5309506","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Reasoning with trees: Faithful question answering over knowledge graph","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:2ba1dfda99af987a0e7a84345a746b3627293f93be134891612abd11d23c3cb5","observation_id":"c5d69635-aff5-44fb-a8ed-f856901ad3db","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Talukdar","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:ffb864b10bfd2c8259fd347e0b430f77f211a6140e6a36672e68ae406606a127","observation_id":"2862f34f-86c0-4a35-b891-8706e465cd93","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"RAGAT: relation aware graph attention network for knowledge graph completion.IEEE Access, 9:20840–20849, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:e8ad501712d02cacb02af3f4bfa9f3d63027ffb5429ff482d74a7b7cd9d5021e","observation_id":"c2f773bc-d157-47c0-b1a3-c2b9822e2ae7","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Knowledge graph reasoning with relational digraph","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:8d99341af651f81575d728d8e3309f8373edb589d3042de482742017d62bd0e0","observation_id":"2a0051b6-4919-44a8-9b15-00c6d80849b5","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Diffusione: Reasoning on knowledge graphs via diffusion-based graph neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:ad2dbcfa0400957ebc66e2739bd9004ff345d401e76410cb6e2b62398826de3c","observation_id":"fda4729c-af01-4c47-a6ee-f026c0a15b9a","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"DRUM: end-to-end differentiable rule mining on knowledge graphs","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:df20087ed24087186849f5df2788e5db88780fdad50e1c04d5530d5d863de610","observation_id":"a5621ce7-73d0-4d52-bcd4-aa7e29d8a428","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Dynamically pruned message passing networks for large-scale knowledge graph reasoning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:5a205e31aa45d303f5083080f9d91b2b42b0ad3c1b5b40f249824b3d11e2ad01","observation_id":"e0e8e3a8-2ba9-4194-87f1-d8e70a7c8481","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Courville","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:1d0b0bb5d5049738b892f30365e624a77506e6e75d2ad5987a507616d9565dc7","observation_id":"cdbf454d-ff34-4d09-8619-c46073906cbd","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Anchoring path for inductive relation prediction in knowledge graphs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:d00de93374e4e99361e3fbe3ad91151f5a870770e389bf9eb53fe057e297233a","observation_id":"32d692f6-3cc7-41dc-a3a5-c42f56821c5a","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Canonical tensor decomposition for knowledge base completion","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:f07072e251677e341c8c7376c5af454915da8e554fa6993b5903761f14a2732e","observation_id":"997186a3-c032-4256-bb49-4bfd9cb631a0","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Translating embeddings for modeling multi-relational data","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:8cf8561806b1916038f5e7037e7aba5362c247a8da46be25bd6f0df6119baa74","observation_id":"0e92213d-fc89-46d4-88e6-b99f331ab0ca","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:7c591782074aa4d349305c4240344334baf34900733c9aa6bbe672a1c362ade1","observation_id":"6d331898-90ae-444a-a73c-a273968e8344","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:604c8c39c7a592130a75b22f5a192ac70561f32e25f370a18ac424f42350e389","observation_id":"5a1fe1f2-a2bd-4a7f-a1a1-226023660efb","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Convolutional 2d knowledge graph embeddings","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:680adb1accbb83e380339ca69ee9747f0080a4c0ca2fad0256607ebe6277008b","observation_id":"950433f5-58f7-4e2f-a12b-4d7991dcd161","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Observed versus latent features for knowledge base and text inference","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:9f1d61ca70a32c4bd4d8ded5752ebe0dd91d8058c223bee7f5ea53b68e8156bd","observation_id":"0301894f-1ec2-4e5e-bc9e-7877a0ad0a55","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"DeepPath: A reinforcement learning method for knowledge graph reasoning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:8f9f29343c9e0fe081ada30eb8360c20209f7019842eaf0f9bde66869f5412bf","observation_id":"f6244897-f5da-4b32-8915-5896800911e7","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Statistical predicate invention","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:ac7e70071e68d91b4f1cacaea4986cc054563e1e39b20c91c5efa6656780a075","observation_id":"bd600a87-7ae7-4082-9205-fc543164869d","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Rotate: Knowledge graph em- bedding by relational rotation in complex space","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:230a8556873d0a21a735a081c178aedcd47ac11df798afee28613a6c064d479a","observation_id":"a8115b36-ba62-4048-916e-81cdcc1127b8","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"House: Knowledge graph embedding with householder parameterization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:4e47b416bd5ebf194a830247d5759afe33c57c67e76bf8c9eb28277bdc962e69","observation_id":"2e21b28e-e789-42c5-927e-5ed35f3800bc","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:d432bb1dd707b350b957ccb4b3f41311dc867653d325dfa3047489bc952e91f6","observation_id":"4aafe9c9-2468-46e2-bf5a-586fd5b7761d","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Multisource hierarchical neural network for knowledge graph embedding.Expert Syst","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:2c517d343bf173e3074d947e1674aa4685c12d3ea456abce4637cf926cd719ae","observation_id":"f6cca351-5d6f-4513-8377-6b035cec9911","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:aa5ff7104fd9dca511ac8b0377a9597d8b7663b25876dea817562564a6976fbc","observation_id":"27ca2113-dd91-463c-8168-b01a662667d6","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"A unified joint approach with topological context learning and rule augmentation for knowledge graph com- pletion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:8cd5bfe0218129450f81ca1a1ee5e26815486926d1e9220b229a3f88d2578d46","observation_id":"776cd89d-01ea-4616-9ee8-453b7fa0176f","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Mixed geometry message and trainable convolutional attention network for knowledge graph completion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:69ba38de228cb7db560dfd91b6d2bc7ebb09b9af89d79646023a369885206eac","observation_id":"7b86543f-53f5-406d-8193-20bf5cc929af","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Multi-relational semantic awareness for knowledge graph completion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:5ec2d139d55ecce1507f030dfc550bf9f94626e3802c8170dfb82a7684358ca7","observation_id":"f45822eb-7ffe-426d-ac96-7c1993e451c2","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Higher-order logical knowl- edge representation learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:2d8d00f0e196ad63311ba65be604030b1025a17abb9815eacd10d27672f308bd","observation_id":"bae21230-91e8-439a-8ac1-d533e1150940","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Teru, Etienne G","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:e514c7dbd22e8d19ba4775f5cf4d672f32fff055520722aff5ec0dd97ddce42a","observation_id":"c717aca1-adf6-4578-99b7-6e6ed1e4648d","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Communicative message passing for inductive relation reasoning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:f3bb69e69924328f608ec1fd11415e9e7006746c7aa85debf9a5cf11391a8448","observation_id":"ea8fb294-dc9d-48b3-9742-820d0d1a326b","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Neural bellman-ford networks: A general graph neural network framework for link prediction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:b98b1ef477a6e3eaef5738642fcec00f9cd4d4b4ee11d03465ce115d5f90d582","observation_id":"72c09ef1-6e26-45cd-a5ae-6a62f4fc0733","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Embedding entities and relations for learning and inference in knowledge bases","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:b166fbbaae9856f2a64a1939ecde3ff122324ca042670886611b19915d7815cb","observation_id":"732d343d-85b6-4c01-aec4-265c69f5e7ec","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Hospedales","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:c28008ca574a605fce7eed45b6f7113df7cfce8c6841712b9253dde13ad5ab58","observation_id":"1b2274b1-551e-4170-a550-42cadd8b0330","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Complex embeddings for simple link prediction","venue":null,"work_id":null,"year":2071},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:9a4774a11fb65ff4375b4ca60378367a13815527fd1b89ce755e307f3b2dfb6e","observation_id":"4f0af17a-8157-46fd-8f27-b2c678792623","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Deeppath: A reinforcement learning method for knowledge graph reasoning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:b5f93f17ff6670e8ae8bd79747e711c37706b48fef4b310e7e2cad40b7c93c7e","observation_id":"4e7f93a1-4c30-478c-9024-b4f445417628","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:560d1835ba0658e9a731355a0e3b8482ee3da82bc2605951d6ea8a9d53043fe0","observation_id":"d0647837-b338-444e-aa95-a3abc70d554f","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Learning to walk with dual agents for knowledge graph reasoning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:10712a9efabb67053c6dff9e6cf983ff310d86e0b29f10dbae883c38dc1ae827","observation_id":"33c535f1-b457-4e95-8812-d87421583a8c","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:8516955a4baba485b63cac0dfe2a1b8e746259b244aa3cd3d2717ced9233e04c","observation_id":"77b8c9b2-6507-4ea7-b06d-405e05cc12d2","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"Pairnorm: Tackling oversmoothing in gnns","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:2eb74e0ad6b481fed3722693059ddc6d69444e83ac60a853dffb8e481e586853","observation_id":"571fb09e-096e-4c40-850f-558b90e211f6","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","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":"2026-06-28T02:26:06.984348Z","title":"type\": \"<TYPE_NAME>","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-28T02:26:06.984348Z"},"links":{"citing_paper":"/paper/2606.05639"},"observation_digest":"sha256:9dccda1ef558892e20e57f79b87367dd50064c8724b13156d1d075b91f49f67b","observation_id":"291bf110-328f-4d61-852d-47b9cd0810c6","resolution":{"observed_at":"2026-06-28T02:26:06.984348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.05639","last_updated":"2026-06-04T03:07:22Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T01:36:20.598898Z","submitted_at":"2026-06-04T03:07:22Z","title":"Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":43,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":43},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2606.05639."}