{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:FX5X4GBJQ3NFDWRG57VJLQBOXP","short_pith_number":"pith:FX5X4GBJ","schema_version":"1.0","canonical_sha256":"2dfb7e182986da51da26efea95c02ebbf4234bf9e5de135d25ea8ecda8a30015","source":{"kind":"arxiv","id":"2209.05698","version":1},"attestation_state":"computed","paper":{"title":"KSG: Knowledge and Skill Graph","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Donglin Wang, Feng Zhao, Ziqi Zhang","submitted_at":"2022-09-13T02:47:46Z","abstract_excerpt":"The knowledge graph (KG) is an essential form of knowledge representation that has grown in prominence in recent years. Because it concentrates on nominal entities and their relationships, traditional knowledge graphs are static and encyclopedic in nature. On this basis, event knowledge graph (Event KG) models the temporal and spatial dynamics by text processing to facilitate downstream applications, such as question-answering, recommendation and intelligent search. Existing KG research, on the other hand, mostly focuses on text processing and static facts, ignoring the vast quantity of dynami"},"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":"2209.05698","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2022-09-13T02:47:46Z","cross_cats_sorted":[],"title_canon_sha256":"45d98b1d24f5c67f4972355c140b5747ce5be330dc284f13b290fed18694bc18","abstract_canon_sha256":"21d429e3f474bb161dc30b3e4aa721ec6f5e00faaece13cf68ca69265c1a88e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:56:49.128745Z","signature_b64":"q6JA0Y8dtoTAq3VCH9gt1i3i6WWISnEudGHIVi/S/OX3/a1onFZ+cZJZHOmIIQWTDqeIb7TRMhaqxbyHpacAAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2dfb7e182986da51da26efea95c02ebbf4234bf9e5de135d25ea8ecda8a30015","last_reissued_at":"2026-07-05T04:56:49.128374Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:56:49.128374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KSG: Knowledge and Skill Graph","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Donglin Wang, Feng Zhao, Ziqi Zhang","submitted_at":"2022-09-13T02:47:46Z","abstract_excerpt":"The knowledge graph (KG) is an essential form of knowledge representation that has grown in prominence in recent years. Because it concentrates on nominal entities and their relationships, traditional knowledge graphs are static and encyclopedic in nature. On this basis, event knowledge graph (Event KG) models the temporal and spatial dynamics by text processing to facilitate downstream applications, such as question-answering, recommendation and intelligent search. Existing KG research, on the other hand, mostly focuses on text processing and static facts, ignoring the vast quantity of dynami"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.05698","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/2209.05698/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":"2209.05698","created_at":"2026-07-05T04:56:49.128421+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.05698v1","created_at":"2026-07-05T04:56:49.128421+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.05698","created_at":"2026-07-05T04:56:49.128421+00:00"},{"alias_kind":"pith_short_12","alias_value":"FX5X4GBJQ3NF","created_at":"2026-07-05T04:56:49.128421+00:00"},{"alias_kind":"pith_short_16","alias_value":"FX5X4GBJQ3NFDWRG","created_at":"2026-07-05T04:56:49.128421+00:00"},{"alias_kind":"pith_short_8","alias_value":"FX5X4GBJ","created_at":"2026-07-05T04:56:49.128421+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/FX5X4GBJQ3NFDWRG57VJLQBOXP","json":"https://pith.science/pith/FX5X4GBJQ3NFDWRG57VJLQBOXP.json","graph_json":"https://pith.science/api/pith-number/FX5X4GBJQ3NFDWRG57VJLQBOXP/graph.json","events_json":"https://pith.science/api/pith-number/FX5X4GBJQ3NFDWRG57VJLQBOXP/events.json","paper":"https://pith.science/paper/FX5X4GBJ"},"agent_actions":{"view_html":"https://pith.science/pith/FX5X4GBJQ3NFDWRG57VJLQBOXP","download_json":"https://pith.science/pith/FX5X4GBJQ3NFDWRG57VJLQBOXP.json","view_paper":"https://pith.science/paper/FX5X4GBJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.05698&json=true","fetch_graph":"https://pith.science/api/pith-number/FX5X4GBJQ3NFDWRG57VJLQBOXP/graph.json","fetch_events":"https://pith.science/api/pith-number/FX5X4GBJQ3NFDWRG57VJLQBOXP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FX5X4GBJQ3NFDWRG57VJLQBOXP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FX5X4GBJQ3NFDWRG57VJLQBOXP/action/storage_attestation","attest_author":"https://pith.science/pith/FX5X4GBJQ3NFDWRG57VJLQBOXP/action/author_attestation","sign_citation":"https://pith.science/pith/FX5X4GBJQ3NFDWRG57VJLQBOXP/action/citation_signature","submit_replication":"https://pith.science/pith/FX5X4GBJQ3NFDWRG57VJLQBOXP/action/replication_record"}},"created_at":"2026-07-05T04:56:49.128421+00:00","updated_at":"2026-07-05T04:56:49.128421+00:00"}