{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:EQTQVXPB32L36CRF5WHXE5ZW4Q","short_pith_number":"pith:EQTQVXPB","schema_version":"1.0","canonical_sha256":"24270adde1de97bf0a25ed8f727736e41732e7f88ccede678b29962d2f29c933","source":{"kind":"arxiv","id":"1709.06907","version":1},"attestation_state":"computed","paper":{"title":"Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.DB"],"primary_cat":"cs.IR","authors_text":"Simon Razniewski, Vevake Balaraman, Werner Nutt","submitted_at":"2017-09-20T14:43:08Z","abstract_excerpt":"In knowledge bases such as Wikidata, it is possible to assert a large set of properties for entities, ranging from generic ones such as name and place of birth to highly profession-specific or background-specific ones such as doctoral advisor or medical condition. Determining a preference or ranking in this large set is a challenge in tasks such as prioritisation of edits or natural-language generation. Most previous approaches to ranking knowledge base properties are purely data-driven, that is, as we show, mistake frequency for interestingness.\n  In this work, we have developed a human-annot"},"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":"1709.06907","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-09-20T14:43:08Z","cross_cats_sorted":["cs.AI","cs.CL","cs.DB"],"title_canon_sha256":"2d1a0ff78ff058819a49fa8ac0373a0be94d66b1b34584c2061370ec3d4a0bbd","abstract_canon_sha256":"c0d33a4bf8e9e3b9c8c3fa0abc44df01d2cd48ed0d3605e7b7b5c8d893a5d98d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:40.111197Z","signature_b64":"BmCoO2fKenwlcM06Z29xYhqnXA+gKl8zWK3MY+q4BBNtKgHqbX+V4Tz+njasepivYMTbD0PRCciaB1u4nv9tAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24270adde1de97bf0a25ed8f727736e41732e7f88ccede678b29962d2f29c933","last_reissued_at":"2026-05-18T00:34:40.110744Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:40.110744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.DB"],"primary_cat":"cs.IR","authors_text":"Simon Razniewski, Vevake Balaraman, Werner Nutt","submitted_at":"2017-09-20T14:43:08Z","abstract_excerpt":"In knowledge bases such as Wikidata, it is possible to assert a large set of properties for entities, ranging from generic ones such as name and place of birth to highly profession-specific or background-specific ones such as doctoral advisor or medical condition. Determining a preference or ranking in this large set is a challenge in tasks such as prioritisation of edits or natural-language generation. Most previous approaches to ranking knowledge base properties are purely data-driven, that is, as we show, mistake frequency for interestingness.\n  In this work, we have developed a human-annot"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.06907","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":""},"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":"1709.06907","created_at":"2026-05-18T00:34:40.110814+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.06907v1","created_at":"2026-05-18T00:34:40.110814+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.06907","created_at":"2026-05-18T00:34:40.110814+00:00"},{"alias_kind":"pith_short_12","alias_value":"EQTQVXPB32L3","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_16","alias_value":"EQTQVXPB32L36CRF","created_at":"2026-05-18T12:31:12.930513+00:00"},{"alias_kind":"pith_short_8","alias_value":"EQTQVXPB","created_at":"2026-05-18T12:31:12.930513+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/EQTQVXPB32L36CRF5WHXE5ZW4Q","json":"https://pith.science/pith/EQTQVXPB32L36CRF5WHXE5ZW4Q.json","graph_json":"https://pith.science/api/pith-number/EQTQVXPB32L36CRF5WHXE5ZW4Q/graph.json","events_json":"https://pith.science/api/pith-number/EQTQVXPB32L36CRF5WHXE5ZW4Q/events.json","paper":"https://pith.science/paper/EQTQVXPB"},"agent_actions":{"view_html":"https://pith.science/pith/EQTQVXPB32L36CRF5WHXE5ZW4Q","download_json":"https://pith.science/pith/EQTQVXPB32L36CRF5WHXE5ZW4Q.json","view_paper":"https://pith.science/paper/EQTQVXPB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.06907&json=true","fetch_graph":"https://pith.science/api/pith-number/EQTQVXPB32L36CRF5WHXE5ZW4Q/graph.json","fetch_events":"https://pith.science/api/pith-number/EQTQVXPB32L36CRF5WHXE5ZW4Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EQTQVXPB32L36CRF5WHXE5ZW4Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EQTQVXPB32L36CRF5WHXE5ZW4Q/action/storage_attestation","attest_author":"https://pith.science/pith/EQTQVXPB32L36CRF5WHXE5ZW4Q/action/author_attestation","sign_citation":"https://pith.science/pith/EQTQVXPB32L36CRF5WHXE5ZW4Q/action/citation_signature","submit_replication":"https://pith.science/pith/EQTQVXPB32L36CRF5WHXE5ZW4Q/action/replication_record"}},"created_at":"2026-05-18T00:34:40.110814+00:00","updated_at":"2026-05-18T00:34:40.110814+00:00"}