{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XGBF5PJNLYHAEPK3CVZBHC6LGY","short_pith_number":"pith:XGBF5PJN","schema_version":"1.0","canonical_sha256":"b9825ebd2d5e0e023d5b1572138bcb361926f0e6c1b21ba45bee5da2654a3a6d","source":{"kind":"arxiv","id":"2504.16537","version":1},"attestation_state":"computed","paper":{"title":"Transformers for Complex Query Answering over Knowledge Hypergraphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hong Ting Tsang, Yangqiu Song, Zihao Wang","submitted_at":"2025-04-23T09:07:21Z","abstract_excerpt":"Complex Query Answering (CQA) has been extensively studied in recent years. In order to model data that is closer to real-world distribution, knowledge graphs with different modalities have been introduced. Triple KGs, as the classic KGs composed of entities and relations of arity 2, have limited representation of real-world facts. Real-world data is more sophisticated. While hyper-relational graphs have been introduced, there are limitations in representing relationships of varying arity that contain entities with equal contributions. To address this gap, we sampled new CQA datasets: JF17k-HC"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.16537","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-23T09:07:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"40ce390a7cbb0d97e58a5c56eb208000ac38e9394ab5c5642bc56bd49249fd08","abstract_canon_sha256":"686d2a98c897e806cdae81782b19b4b6751471f8f6755ab58b16f367ad8ae2c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:01.260702Z","signature_b64":"E7fMQKi7eDZvSkVTg+8NgLlpvXNx67RPsNUGFeBtuUTzOg7GX3K0wdiAErb8WdWFn9mRBpToHxytBaTOhGjcBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9825ebd2d5e0e023d5b1572138bcb361926f0e6c1b21ba45bee5da2654a3a6d","last_reissued_at":"2026-07-05T10:53:01.260286Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:01.260286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transformers for Complex Query Answering over Knowledge Hypergraphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hong Ting Tsang, Yangqiu Song, Zihao Wang","submitted_at":"2025-04-23T09:07:21Z","abstract_excerpt":"Complex Query Answering (CQA) has been extensively studied in recent years. In order to model data that is closer to real-world distribution, knowledge graphs with different modalities have been introduced. Triple KGs, as the classic KGs composed of entities and relations of arity 2, have limited representation of real-world facts. Real-world data is more sophisticated. While hyper-relational graphs have been introduced, there are limitations in representing relationships of varying arity that contain entities with equal contributions. To address this gap, we sampled new CQA datasets: JF17k-HC"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.16537","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.16537/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.16537","created_at":"2026-07-05T10:53:01.260350+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.16537v1","created_at":"2026-07-05T10:53:01.260350+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.16537","created_at":"2026-07-05T10:53:01.260350+00:00"},{"alias_kind":"pith_short_12","alias_value":"XGBF5PJNLYHA","created_at":"2026-07-05T10:53:01.260350+00:00"},{"alias_kind":"pith_short_16","alias_value":"XGBF5PJNLYHAEPK3","created_at":"2026-07-05T10:53:01.260350+00:00"},{"alias_kind":"pith_short_8","alias_value":"XGBF5PJN","created_at":"2026-07-05T10:53:01.260350+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.00759","citing_title":"Enhancing Transformers for Generalizable First-Order Logical Entailment","ref_index":55,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XGBF5PJNLYHAEPK3CVZBHC6LGY","json":"https://pith.science/pith/XGBF5PJNLYHAEPK3CVZBHC6LGY.json","graph_json":"https://pith.science/api/pith-number/XGBF5PJNLYHAEPK3CVZBHC6LGY/graph.json","events_json":"https://pith.science/api/pith-number/XGBF5PJNLYHAEPK3CVZBHC6LGY/events.json","paper":"https://pith.science/paper/XGBF5PJN"},"agent_actions":{"view_html":"https://pith.science/pith/XGBF5PJNLYHAEPK3CVZBHC6LGY","download_json":"https://pith.science/pith/XGBF5PJNLYHAEPK3CVZBHC6LGY.json","view_paper":"https://pith.science/paper/XGBF5PJN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.16537&json=true","fetch_graph":"https://pith.science/api/pith-number/XGBF5PJNLYHAEPK3CVZBHC6LGY/graph.json","fetch_events":"https://pith.science/api/pith-number/XGBF5PJNLYHAEPK3CVZBHC6LGY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XGBF5PJNLYHAEPK3CVZBHC6LGY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XGBF5PJNLYHAEPK3CVZBHC6LGY/action/storage_attestation","attest_author":"https://pith.science/pith/XGBF5PJNLYHAEPK3CVZBHC6LGY/action/author_attestation","sign_citation":"https://pith.science/pith/XGBF5PJNLYHAEPK3CVZBHC6LGY/action/citation_signature","submit_replication":"https://pith.science/pith/XGBF5PJNLYHAEPK3CVZBHC6LGY/action/replication_record"}},"created_at":"2026-07-05T10:53:01.260350+00:00","updated_at":"2026-07-05T10:53:01.260350+00:00"}