{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HTUJTK3YXQDKTLQYB6FDVOS6RS","short_pith_number":"pith:HTUJTK3Y","schema_version":"1.0","canonical_sha256":"3ce899ab78bc06a9ae180f8a3aba5e8c91966c27f1f4941b322b525ed235a4a1","source":{"kind":"arxiv","id":"2402.03291","version":1},"attestation_state":"computed","paper":{"title":"Knowledge Acquisition and Integration with Expert-in-the-loop","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.HC","authors_text":"Dan Zhang, Estevam Hruschka, Frederick Choi, Hannah Kim, Sajjadur Rahman","submitted_at":"2024-02-05T18:49:55Z","abstract_excerpt":"Constructing and serving knowledge graphs (KGs) is an iterative and human-centered process involving on-demand programming and analysis. In this paper, we present Kyurem, a programmable and interactive widget library that facilitates human-in-the-loop knowledge acquisition and integration to enable continuous curation a knowledge graph (KG). Kyurem provides a seamless environment within computational notebooks where data scientists explore a KG to identify opportunities for acquiring new knowledge and verify recommendations provided by AI agents for integrating the acquired knowledge in the KG"},"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":"2402.03291","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.HC","submitted_at":"2024-02-05T18:49:55Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"7061e4c000793e476c3e7d740136a7150bc3abe0c6a88c9d6976d37c5690fd38","abstract_canon_sha256":"f6e83df49ba4b8c9cab41f5e9035e3dde1bcd3102cbf23fbb3bb183931bda734"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:41:33.604411Z","signature_b64":"ot/N8cPH9rgopvrCp7tfrWkRnr2OGhNfOdIIyac5KvBXSvDX2DIh57mhExuOhlT8Q3/9BSr1/TjWTV2oC28PBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ce899ab78bc06a9ae180f8a3aba5e8c91966c27f1f4941b322b525ed235a4a1","last_reissued_at":"2026-07-05T07:41:33.603946Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:41:33.603946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Knowledge Acquisition and Integration with Expert-in-the-loop","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.HC","authors_text":"Dan Zhang, Estevam Hruschka, Frederick Choi, Hannah Kim, Sajjadur Rahman","submitted_at":"2024-02-05T18:49:55Z","abstract_excerpt":"Constructing and serving knowledge graphs (KGs) is an iterative and human-centered process involving on-demand programming and analysis. In this paper, we present Kyurem, a programmable and interactive widget library that facilitates human-in-the-loop knowledge acquisition and integration to enable continuous curation a knowledge graph (KG). Kyurem provides a seamless environment within computational notebooks where data scientists explore a KG to identify opportunities for acquiring new knowledge and verify recommendations provided by AI agents for integrating the acquired knowledge in the KG"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.03291","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/2402.03291/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":"2402.03291","created_at":"2026-07-05T07:41:33.604009+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.03291v1","created_at":"2026-07-05T07:41:33.604009+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.03291","created_at":"2026-07-05T07:41:33.604009+00:00"},{"alias_kind":"pith_short_12","alias_value":"HTUJTK3YXQDK","created_at":"2026-07-05T07:41:33.604009+00:00"},{"alias_kind":"pith_short_16","alias_value":"HTUJTK3YXQDKTLQY","created_at":"2026-07-05T07:41:33.604009+00:00"},{"alias_kind":"pith_short_8","alias_value":"HTUJTK3Y","created_at":"2026-07-05T07:41:33.604009+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17500","citing_title":"The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapes","ref_index":64,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HTUJTK3YXQDKTLQYB6FDVOS6RS","json":"https://pith.science/pith/HTUJTK3YXQDKTLQYB6FDVOS6RS.json","graph_json":"https://pith.science/api/pith-number/HTUJTK3YXQDKTLQYB6FDVOS6RS/graph.json","events_json":"https://pith.science/api/pith-number/HTUJTK3YXQDKTLQYB6FDVOS6RS/events.json","paper":"https://pith.science/paper/HTUJTK3Y"},"agent_actions":{"view_html":"https://pith.science/pith/HTUJTK3YXQDKTLQYB6FDVOS6RS","download_json":"https://pith.science/pith/HTUJTK3YXQDKTLQYB6FDVOS6RS.json","view_paper":"https://pith.science/paper/HTUJTK3Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.03291&json=true","fetch_graph":"https://pith.science/api/pith-number/HTUJTK3YXQDKTLQYB6FDVOS6RS/graph.json","fetch_events":"https://pith.science/api/pith-number/HTUJTK3YXQDKTLQYB6FDVOS6RS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HTUJTK3YXQDKTLQYB6FDVOS6RS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HTUJTK3YXQDKTLQYB6FDVOS6RS/action/storage_attestation","attest_author":"https://pith.science/pith/HTUJTK3YXQDKTLQYB6FDVOS6RS/action/author_attestation","sign_citation":"https://pith.science/pith/HTUJTK3YXQDKTLQYB6FDVOS6RS/action/citation_signature","submit_replication":"https://pith.science/pith/HTUJTK3YXQDKTLQYB6FDVOS6RS/action/replication_record"}},"created_at":"2026-07-05T07:41:33.604009+00:00","updated_at":"2026-07-05T07:41:33.604009+00:00"}