{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:JXOQYMF7CN2CUTZMZQBHLXJO3C","short_pith_number":"pith:JXOQYMF7","schema_version":"1.0","canonical_sha256":"4ddd0c30bf13742a4f2ccc0275dd2ed8946118c6f7eaf569e297e933d44f4883","source":{"kind":"arxiv","id":"1402.5988","version":2},"attestation_state":"computed","paper":{"title":"Incremental Learning of Event Definitions with Inductive Logic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alexander Artikis, George Paliouras, Nikos Katzouris","submitted_at":"2014-02-24T21:22:51Z","abstract_excerpt":"Event recognition systems rely on properly engineered knowledge bases of event definitions to infer occurrences of events in time. The manual development of such knowledge is a tedious and error-prone task, thus event-based applications may benefit from automated knowledge construction techniques, such as Inductive Logic Programming (ILP), which combines machine learning with the declarative and formal semantics of First-Order Logic. However, learning temporal logical formalisms, which are typically utilized by logic-based Event Recognition systems is a challenging task, which most ILP systems"},"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":"1402.5988","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-02-24T21:22:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"20970561d25722eb224e0992ff4f073baf45e92eb98c94b1b2021f0e6f89cd4c","abstract_canon_sha256":"916ccc0afd725f8eedc676d968b7d795493a74be14f4db28ac36590247bd2567"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:33:01.293379Z","signature_b64":"Rd5gb340UKGGrHX2qCsSC9RtGUBwomVxDCHY8QHmaJRgak8Yo3ej+I+55bEcEQbH5zOJS4K9Oi0j/46uEfKeBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ddd0c30bf13742a4f2ccc0275dd2ed8946118c6f7eaf569e297e933d44f4883","last_reissued_at":"2026-05-18T02:33:01.293031Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:33:01.293031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Incremental Learning of Event Definitions with Inductive Logic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alexander Artikis, George Paliouras, Nikos Katzouris","submitted_at":"2014-02-24T21:22:51Z","abstract_excerpt":"Event recognition systems rely on properly engineered knowledge bases of event definitions to infer occurrences of events in time. The manual development of such knowledge is a tedious and error-prone task, thus event-based applications may benefit from automated knowledge construction techniques, such as Inductive Logic Programming (ILP), which combines machine learning with the declarative and formal semantics of First-Order Logic. However, learning temporal logical formalisms, which are typically utilized by logic-based Event Recognition systems is a challenging task, which most ILP systems"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.5988","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":""},"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":"1402.5988","created_at":"2026-05-18T02:33:01.293081+00:00"},{"alias_kind":"arxiv_version","alias_value":"1402.5988v2","created_at":"2026-05-18T02:33:01.293081+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.5988","created_at":"2026-05-18T02:33:01.293081+00:00"},{"alias_kind":"pith_short_12","alias_value":"JXOQYMF7CN2C","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_16","alias_value":"JXOQYMF7CN2CUTZM","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_8","alias_value":"JXOQYMF7","created_at":"2026-05-18T12:28:35.611951+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/JXOQYMF7CN2CUTZMZQBHLXJO3C","json":"https://pith.science/pith/JXOQYMF7CN2CUTZMZQBHLXJO3C.json","graph_json":"https://pith.science/api/pith-number/JXOQYMF7CN2CUTZMZQBHLXJO3C/graph.json","events_json":"https://pith.science/api/pith-number/JXOQYMF7CN2CUTZMZQBHLXJO3C/events.json","paper":"https://pith.science/paper/JXOQYMF7"},"agent_actions":{"view_html":"https://pith.science/pith/JXOQYMF7CN2CUTZMZQBHLXJO3C","download_json":"https://pith.science/pith/JXOQYMF7CN2CUTZMZQBHLXJO3C.json","view_paper":"https://pith.science/paper/JXOQYMF7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1402.5988&json=true","fetch_graph":"https://pith.science/api/pith-number/JXOQYMF7CN2CUTZMZQBHLXJO3C/graph.json","fetch_events":"https://pith.science/api/pith-number/JXOQYMF7CN2CUTZMZQBHLXJO3C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JXOQYMF7CN2CUTZMZQBHLXJO3C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JXOQYMF7CN2CUTZMZQBHLXJO3C/action/storage_attestation","attest_author":"https://pith.science/pith/JXOQYMF7CN2CUTZMZQBHLXJO3C/action/author_attestation","sign_citation":"https://pith.science/pith/JXOQYMF7CN2CUTZMZQBHLXJO3C/action/citation_signature","submit_replication":"https://pith.science/pith/JXOQYMF7CN2CUTZMZQBHLXJO3C/action/replication_record"}},"created_at":"2026-05-18T02:33:01.293081+00:00","updated_at":"2026-05-18T02:33:01.293081+00:00"}