{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LEX2UQYUSUALHFL3BAPRK52IQW","short_pith_number":"pith:LEX2UQYU","schema_version":"1.0","canonical_sha256":"592faa43149500b3957b081f15774885a4118d089f5753635ff666c21ff1cbcf","source":{"kind":"arxiv","id":"2505.09246","version":4},"attestation_state":"computed","paper":{"title":"Autofocus Retrieval: An Effective Pipeline for Multi-Hop Question Answering With Semi-Structured Knowledge","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Derian Boer, Stefan Kramer, Stephen Roth","submitted_at":"2025-05-14T09:35:56Z","abstract_excerpt":"In many real-world settings, machine learning models and interactive systems have access to both structured knowledge, e.g., knowledge graphs or tables, and unstructured content, e.g., natural language documents. Yet, most rely on either. Semi-Structured Knowledge Bases (SKBs) bridge this gap by linking unstructured content to nodes within structured data. In this work, we present Autofocus-Retriever (AF-Retriever), a modular framework for SKB-based, multi-hop question answering. It combines structural and textual retrieval through novel integration steps and optimizations, achieving the best "},"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":"2505.09246","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-05-14T09:35:56Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"51fb86ceb2a8a2978e82863411110c950d4fc63a67cb30f291301c2b49a87a50","abstract_canon_sha256":"9fd17b88d05be4b7719a0be1daefee6aec6b31fca0878e2843bb55df774d63e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:01.365745Z","signature_b64":"2jrPEt4TB6uWwhx5paDLZsCEy2TMR/kYTtN7GRQfc7MIQnQNAPLnpXhJKo8Q6T0Zg3fJ/fF3wiC9MP6hh6miCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"592faa43149500b3957b081f15774885a4118d089f5753635ff666c21ff1cbcf","last_reissued_at":"2026-05-17T23:39:01.365034Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:01.365034Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Autofocus Retrieval: An Effective Pipeline for Multi-Hop Question Answering With Semi-Structured Knowledge","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Derian Boer, Stefan Kramer, Stephen Roth","submitted_at":"2025-05-14T09:35:56Z","abstract_excerpt":"In many real-world settings, machine learning models and interactive systems have access to both structured knowledge, e.g., knowledge graphs or tables, and unstructured content, e.g., natural language documents. Yet, most rely on either. Semi-Structured Knowledge Bases (SKBs) bridge this gap by linking unstructured content to nodes within structured data. In this work, we present Autofocus-Retriever (AF-Retriever), a modular framework for SKB-based, multi-hop question answering. It combines structural and textual retrieval through novel integration steps and optimizations, achieving the best "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09246","kind":"arxiv","version":4},"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":"2505.09246","created_at":"2026-05-17T23:39:01.365166+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.09246v4","created_at":"2026-05-17T23:39:01.365166+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09246","created_at":"2026-05-17T23:39:01.365166+00:00"},{"alias_kind":"pith_short_12","alias_value":"LEX2UQYUSUAL","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"LEX2UQYUSUALHFL3","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"LEX2UQYU","created_at":"2026-05-18T12:33:37.589309+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/LEX2UQYUSUALHFL3BAPRK52IQW","json":"https://pith.science/pith/LEX2UQYUSUALHFL3BAPRK52IQW.json","graph_json":"https://pith.science/api/pith-number/LEX2UQYUSUALHFL3BAPRK52IQW/graph.json","events_json":"https://pith.science/api/pith-number/LEX2UQYUSUALHFL3BAPRK52IQW/events.json","paper":"https://pith.science/paper/LEX2UQYU"},"agent_actions":{"view_html":"https://pith.science/pith/LEX2UQYUSUALHFL3BAPRK52IQW","download_json":"https://pith.science/pith/LEX2UQYUSUALHFL3BAPRK52IQW.json","view_paper":"https://pith.science/paper/LEX2UQYU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.09246&json=true","fetch_graph":"https://pith.science/api/pith-number/LEX2UQYUSUALHFL3BAPRK52IQW/graph.json","fetch_events":"https://pith.science/api/pith-number/LEX2UQYUSUALHFL3BAPRK52IQW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LEX2UQYUSUALHFL3BAPRK52IQW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LEX2UQYUSUALHFL3BAPRK52IQW/action/storage_attestation","attest_author":"https://pith.science/pith/LEX2UQYUSUALHFL3BAPRK52IQW/action/author_attestation","sign_citation":"https://pith.science/pith/LEX2UQYUSUALHFL3BAPRK52IQW/action/citation_signature","submit_replication":"https://pith.science/pith/LEX2UQYUSUALHFL3BAPRK52IQW/action/replication_record"}},"created_at":"2026-05-17T23:39:01.365166+00:00","updated_at":"2026-05-17T23:39:01.365166+00:00"}