{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SQQLYZVWO7JXT2HYA44GLYK76E","short_pith_number":"pith:SQQLYZVW","schema_version":"1.0","canonical_sha256":"9420bc66b677d379e8f8073865e15ff13ac989a068f89777b966daa2cee35485","source":{"kind":"arxiv","id":"2411.15694","version":2},"attestation_state":"computed","paper":{"title":"Deep Sparse Latent Feature Models for Knowledge Graph Completion","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bailing Wang, Bin Yu, Haotian Li, Kai Wang, Lingzhi Wang, Richard Yi Da Xu, Rui Zhang, youwei wang, Yuliang Wei","submitted_at":"2024-11-24T03:17:37Z","abstract_excerpt":"Recent advances in knowledge graph completion (KGC) have emphasized text-based approaches to navigate the inherent complexities of large-scale knowledge graphs (KGs). While these methods have achieved notable progress, they frequently struggle to fully incorporate the global structural properties of the graph. Stochastic blockmodels (SBMs), especially the latent feature relational model (LFRM), offer robust probabilistic frameworks for identifying latent community structures and improving link prediction. This paper presents a novel probabilistic KGC framework utilizing sparse latent feature m"},"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":"2411.15694","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-24T03:17:37Z","cross_cats_sorted":[],"title_canon_sha256":"915d9ab921d12fd4962eee07d7c50304fd7fb24c9123c2a24dd94cfa3e6985dd","abstract_canon_sha256":"45667a5be7db7c07a8b4b4ae7612fff8d1f9e434ec85af291cd87db181f45298"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:20:48.207267Z","signature_b64":"hPX+3bhDdJ1HuuNYd/L3HATvWO5/Oy/YhU5kLCGLq9MwGPw2r1SZV+ilYsGgZC9uYD0lh2BsYTQyP96Ulc0SCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9420bc66b677d379e8f8073865e15ff13ac989a068f89777b966daa2cee35485","last_reissued_at":"2026-07-05T11:20:48.206813Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:20:48.206813Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Sparse Latent Feature Models for Knowledge Graph Completion","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bailing Wang, Bin Yu, Haotian Li, Kai Wang, Lingzhi Wang, Richard Yi Da Xu, Rui Zhang, youwei wang, Yuliang Wei","submitted_at":"2024-11-24T03:17:37Z","abstract_excerpt":"Recent advances in knowledge graph completion (KGC) have emphasized text-based approaches to navigate the inherent complexities of large-scale knowledge graphs (KGs). While these methods have achieved notable progress, they frequently struggle to fully incorporate the global structural properties of the graph. Stochastic blockmodels (SBMs), especially the latent feature relational model (LFRM), offer robust probabilistic frameworks for identifying latent community structures and improving link prediction. This paper presents a novel probabilistic KGC framework utilizing sparse latent feature m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15694","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/2411.15694/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":"2411.15694","created_at":"2026-07-05T11:20:48.206880+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.15694v2","created_at":"2026-07-05T11:20:48.206880+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15694","created_at":"2026-07-05T11:20:48.206880+00:00"},{"alias_kind":"pith_short_12","alias_value":"SQQLYZVWO7JX","created_at":"2026-07-05T11:20:48.206880+00:00"},{"alias_kind":"pith_short_16","alias_value":"SQQLYZVWO7JXT2HY","created_at":"2026-07-05T11:20:48.206880+00:00"},{"alias_kind":"pith_short_8","alias_value":"SQQLYZVW","created_at":"2026-07-05T11:20:48.206880+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SQQLYZVWO7JXT2HYA44GLYK76E","json":"https://pith.science/pith/SQQLYZVWO7JXT2HYA44GLYK76E.json","graph_json":"https://pith.science/api/pith-number/SQQLYZVWO7JXT2HYA44GLYK76E/graph.json","events_json":"https://pith.science/api/pith-number/SQQLYZVWO7JXT2HYA44GLYK76E/events.json","paper":"https://pith.science/paper/SQQLYZVW"},"agent_actions":{"view_html":"https://pith.science/pith/SQQLYZVWO7JXT2HYA44GLYK76E","download_json":"https://pith.science/pith/SQQLYZVWO7JXT2HYA44GLYK76E.json","view_paper":"https://pith.science/paper/SQQLYZVW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.15694&json=true","fetch_graph":"https://pith.science/api/pith-number/SQQLYZVWO7JXT2HYA44GLYK76E/graph.json","fetch_events":"https://pith.science/api/pith-number/SQQLYZVWO7JXT2HYA44GLYK76E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SQQLYZVWO7JXT2HYA44GLYK76E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SQQLYZVWO7JXT2HYA44GLYK76E/action/storage_attestation","attest_author":"https://pith.science/pith/SQQLYZVWO7JXT2HYA44GLYK76E/action/author_attestation","sign_citation":"https://pith.science/pith/SQQLYZVWO7JXT2HYA44GLYK76E/action/citation_signature","submit_replication":"https://pith.science/pith/SQQLYZVWO7JXT2HYA44GLYK76E/action/replication_record"}},"created_at":"2026-07-05T11:20:48.206880+00:00","updated_at":"2026-07-05T11:20:48.206880+00:00"}