{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6O3VHD5KVMC5Y7XIA7PTPKVU4K","short_pith_number":"pith:6O3VHD5K","schema_version":"1.0","canonical_sha256":"f3b7538faaab05dc7ee807df37aab4e28fe2c0d5eed38e7ba8d9c6542b564823","source":{"kind":"arxiv","id":"2409.12865","version":2},"attestation_state":"computed","paper":{"title":"KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Jianxin Li, Junnan Liu, Qianren Mao, Weifeng Jiang","submitted_at":"2024-09-19T16:08:10Z","abstract_excerpt":"Knowledge graph reasoning plays a vital role in various applications and has garnered considerable attention. Recently, path-based methods have achieved impressive performance. However, they may face limitations stemming from constraints in message-passing neural networks, such as missing paths and information over-squashing. In this paper, we revisit the application of transformers for knowledge graph reasoning to address the constraints faced by path-based methods and propose a novel method KnowFormer. KnowFormer utilizes a transformer architecture to perform reasoning on knowledge graphs fr"},"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":"2409.12865","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-09-19T16:08:10Z","cross_cats_sorted":[],"title_canon_sha256":"2e11aaee76e55f33e8a939e888b5a68bc7e0c5544c4684b9a268e7efa7ff9427","abstract_canon_sha256":"10e9da1d4be10c21c573dae91a5f177cfa2e5441fac9a80fff602c5bb1c20a8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:33.605775Z","signature_b64":"QFVNRNrtMYvGNoUa3w/8DcoRk9DaMZGLpL+8xpZdCiGIlJARHzd2Y7USaXFDS/8TsCZoD62qsMwayucxk7y2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f3b7538faaab05dc7ee807df37aab4e28fe2c0d5eed38e7ba8d9c6542b564823","last_reissued_at":"2026-07-05T09:50:33.605251Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:33.605251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Jianxin Li, Junnan Liu, Qianren Mao, Weifeng Jiang","submitted_at":"2024-09-19T16:08:10Z","abstract_excerpt":"Knowledge graph reasoning plays a vital role in various applications and has garnered considerable attention. Recently, path-based methods have achieved impressive performance. However, they may face limitations stemming from constraints in message-passing neural networks, such as missing paths and information over-squashing. In this paper, we revisit the application of transformers for knowledge graph reasoning to address the constraints faced by path-based methods and propose a novel method KnowFormer. KnowFormer utilizes a transformer architecture to perform reasoning on knowledge graphs fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.12865","kind":"arxiv","version":2},"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/2409.12865/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":"2409.12865","created_at":"2026-07-05T09:50:33.605307+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.12865v2","created_at":"2026-07-05T09:50:33.605307+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.12865","created_at":"2026-07-05T09:50:33.605307+00:00"},{"alias_kind":"pith_short_12","alias_value":"6O3VHD5KVMC5","created_at":"2026-07-05T09:50:33.605307+00:00"},{"alias_kind":"pith_short_16","alias_value":"6O3VHD5KVMC5Y7XI","created_at":"2026-07-05T09:50:33.605307+00:00"},{"alias_kind":"pith_short_8","alias_value":"6O3VHD5K","created_at":"2026-07-05T09:50:33.605307+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08658","citing_title":"Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6O3VHD5KVMC5Y7XIA7PTPKVU4K","json":"https://pith.science/pith/6O3VHD5KVMC5Y7XIA7PTPKVU4K.json","graph_json":"https://pith.science/api/pith-number/6O3VHD5KVMC5Y7XIA7PTPKVU4K/graph.json","events_json":"https://pith.science/api/pith-number/6O3VHD5KVMC5Y7XIA7PTPKVU4K/events.json","paper":"https://pith.science/paper/6O3VHD5K"},"agent_actions":{"view_html":"https://pith.science/pith/6O3VHD5KVMC5Y7XIA7PTPKVU4K","download_json":"https://pith.science/pith/6O3VHD5KVMC5Y7XIA7PTPKVU4K.json","view_paper":"https://pith.science/paper/6O3VHD5K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.12865&json=true","fetch_graph":"https://pith.science/api/pith-number/6O3VHD5KVMC5Y7XIA7PTPKVU4K/graph.json","fetch_events":"https://pith.science/api/pith-number/6O3VHD5KVMC5Y7XIA7PTPKVU4K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6O3VHD5KVMC5Y7XIA7PTPKVU4K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6O3VHD5KVMC5Y7XIA7PTPKVU4K/action/storage_attestation","attest_author":"https://pith.science/pith/6O3VHD5KVMC5Y7XIA7PTPKVU4K/action/author_attestation","sign_citation":"https://pith.science/pith/6O3VHD5KVMC5Y7XIA7PTPKVU4K/action/citation_signature","submit_replication":"https://pith.science/pith/6O3VHD5KVMC5Y7XIA7PTPKVU4K/action/replication_record"}},"created_at":"2026-07-05T09:50:33.605307+00:00","updated_at":"2026-07-05T09:50:33.605307+00:00"}