{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KR652SLP7GQUIQNXGZ4WC4N5DL","short_pith_number":"pith:KR652SLP","schema_version":"1.0","canonical_sha256":"547ddd496ff9a14441b736796171bd1addcc22be220cff284af8db2de6a40237","source":{"kind":"arxiv","id":"2501.11301","version":3},"attestation_state":"computed","paper":{"title":"Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Santhosh Thottingal","submitted_at":"2025-01-20T07:05:15Z","abstract_excerpt":"This paper introduces an approach to question answering over knowledge bases like Wikipedia and Wikidata by performing \"question-to-question\" matching and retrieval from a dense vector embedding store. Instead of embedding document content, we generate a comprehensive set of questions for each logical content unit using an instruction-tuned LLM. These questions are vector-embedded and stored, mapping to the corresponding content. Vector embedding of user queries are then matched against this question vector store. The highest similarity score leads to direct retrieval of the associated article"},"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":"2501.11301","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-20T07:05:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6806ab2584c71caa3d65267eed8a3f32ca7cab44418c895839b5d3ae848a3fb6","abstract_canon_sha256":"cfd86e7c52a627b2df31266bf59a1a634a26a1418a451095ef246aa9c060b31c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:53.234920Z","signature_b64":"gvOc2Hl8BFNZEbSfrR3/4xZ7eoOjy6No6ekywlDGmtqvSYQyloiOha2zaoD0Iv5EU2tzoqsMi9KkMuLw3NLSAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"547ddd496ff9a14441b736796171bd1addcc22be220cff284af8db2de6a40237","last_reissued_at":"2026-07-05T10:17:53.234439Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:53.234439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Santhosh Thottingal","submitted_at":"2025-01-20T07:05:15Z","abstract_excerpt":"This paper introduces an approach to question answering over knowledge bases like Wikipedia and Wikidata by performing \"question-to-question\" matching and retrieval from a dense vector embedding store. Instead of embedding document content, we generate a comprehensive set of questions for each logical content unit using an instruction-tuned LLM. These questions are vector-embedded and stored, mapping to the corresponding content. Vector embedding of user queries are then matched against this question vector store. The highest similarity score leads to direct retrieval of the associated article"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.11301","kind":"arxiv","version":3},"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/2501.11301/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":"2501.11301","created_at":"2026-07-05T10:17:53.234498+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.11301v3","created_at":"2026-07-05T10:17:53.234498+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.11301","created_at":"2026-07-05T10:17:53.234498+00:00"},{"alias_kind":"pith_short_12","alias_value":"KR652SLP7GQU","created_at":"2026-07-05T10:17:53.234498+00:00"},{"alias_kind":"pith_short_16","alias_value":"KR652SLP7GQUIQNX","created_at":"2026-07-05T10:17:53.234498+00:00"},{"alias_kind":"pith_short_8","alias_value":"KR652SLP","created_at":"2026-07-05T10:17:53.234498+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/KR652SLP7GQUIQNXGZ4WC4N5DL","json":"https://pith.science/pith/KR652SLP7GQUIQNXGZ4WC4N5DL.json","graph_json":"https://pith.science/api/pith-number/KR652SLP7GQUIQNXGZ4WC4N5DL/graph.json","events_json":"https://pith.science/api/pith-number/KR652SLP7GQUIQNXGZ4WC4N5DL/events.json","paper":"https://pith.science/paper/KR652SLP"},"agent_actions":{"view_html":"https://pith.science/pith/KR652SLP7GQUIQNXGZ4WC4N5DL","download_json":"https://pith.science/pith/KR652SLP7GQUIQNXGZ4WC4N5DL.json","view_paper":"https://pith.science/paper/KR652SLP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.11301&json=true","fetch_graph":"https://pith.science/api/pith-number/KR652SLP7GQUIQNXGZ4WC4N5DL/graph.json","fetch_events":"https://pith.science/api/pith-number/KR652SLP7GQUIQNXGZ4WC4N5DL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KR652SLP7GQUIQNXGZ4WC4N5DL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KR652SLP7GQUIQNXGZ4WC4N5DL/action/storage_attestation","attest_author":"https://pith.science/pith/KR652SLP7GQUIQNXGZ4WC4N5DL/action/author_attestation","sign_citation":"https://pith.science/pith/KR652SLP7GQUIQNXGZ4WC4N5DL/action/citation_signature","submit_replication":"https://pith.science/pith/KR652SLP7GQUIQNXGZ4WC4N5DL/action/replication_record"}},"created_at":"2026-07-05T10:17:53.234498+00:00","updated_at":"2026-07-05T10:17:53.234498+00:00"}