{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:USKCBQLJZPNC57SEWD7LLC3PIU","short_pith_number":"pith:USKCBQLJ","schema_version":"1.0","canonical_sha256":"a49420c169cbda2efe44b0feb58b6f4532562c05311768c291a4d8f9ea3075f1","source":{"kind":"arxiv","id":"2111.00732","version":2},"attestation_state":"computed","paper":{"title":"Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Guilin Qi, Huiying Li, Tenggou Wang, Tianxing Wu, Yongrui Chen","submitted_at":"2021-11-01T07:08:46Z","abstract_excerpt":"Query graph construction aims to construct the correct executable SPARQL on the KG to answer natural language questions. Although recent methods have achieved good results using neural network-based query graph ranking, they suffer from three new challenges when handling more complex questions: 1) complicated SPARQL syntax, 2) huge search space, and 3) locally ambiguous query graphs. In this paper, we provide a new solution. As a preparation, we extend the query graph by treating each SPARQL clause as a subgraph consisting of vertices and edges and define a unified graph grammar called AQG to "},"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":"2111.00732","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2021-11-01T07:08:46Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"0389c1de53a9bba292720dc9f794073b5ad4b8f0e41e7f6119bd7eb97363c829","abstract_canon_sha256":"4a4dd715123b27d640f92e57b67e61777abfb54a07d77279520fb110d49d58b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:56:12.651446Z","signature_b64":"qkYEitIhU8LUmCbTdieKy6BkGUV6wmAqf7D0yzw8H+D3V7pzw1qPpNxp5IL6J9cpcV/+fBmk7toX8YMH2jehDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a49420c169cbda2efe44b0feb58b6f4532562c05311768c291a4d8f9ea3075f1","last_reissued_at":"2026-07-05T04:56:12.651017Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:56:12.651017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Guilin Qi, Huiying Li, Tenggou Wang, Tianxing Wu, Yongrui Chen","submitted_at":"2021-11-01T07:08:46Z","abstract_excerpt":"Query graph construction aims to construct the correct executable SPARQL on the KG to answer natural language questions. Although recent methods have achieved good results using neural network-based query graph ranking, they suffer from three new challenges when handling more complex questions: 1) complicated SPARQL syntax, 2) huge search space, and 3) locally ambiguous query graphs. In this paper, we provide a new solution. As a preparation, we extend the query graph by treating each SPARQL clause as a subgraph consisting of vertices and edges and define a unified graph grammar called AQG to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.00732","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2111.00732/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":"2111.00732","created_at":"2026-07-05T04:56:12.651072+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.00732v2","created_at":"2026-07-05T04:56:12.651072+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.00732","created_at":"2026-07-05T04:56:12.651072+00:00"},{"alias_kind":"pith_short_12","alias_value":"USKCBQLJZPNC","created_at":"2026-07-05T04:56:12.651072+00:00"},{"alias_kind":"pith_short_16","alias_value":"USKCBQLJZPNC57SE","created_at":"2026-07-05T04:56:12.651072+00:00"},{"alias_kind":"pith_short_8","alias_value":"USKCBQLJ","created_at":"2026-07-05T04:56:12.651072+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/USKCBQLJZPNC57SEWD7LLC3PIU","json":"https://pith.science/pith/USKCBQLJZPNC57SEWD7LLC3PIU.json","graph_json":"https://pith.science/api/pith-number/USKCBQLJZPNC57SEWD7LLC3PIU/graph.json","events_json":"https://pith.science/api/pith-number/USKCBQLJZPNC57SEWD7LLC3PIU/events.json","paper":"https://pith.science/paper/USKCBQLJ"},"agent_actions":{"view_html":"https://pith.science/pith/USKCBQLJZPNC57SEWD7LLC3PIU","download_json":"https://pith.science/pith/USKCBQLJZPNC57SEWD7LLC3PIU.json","view_paper":"https://pith.science/paper/USKCBQLJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.00732&json=true","fetch_graph":"https://pith.science/api/pith-number/USKCBQLJZPNC57SEWD7LLC3PIU/graph.json","fetch_events":"https://pith.science/api/pith-number/USKCBQLJZPNC57SEWD7LLC3PIU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/USKCBQLJZPNC57SEWD7LLC3PIU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/USKCBQLJZPNC57SEWD7LLC3PIU/action/storage_attestation","attest_author":"https://pith.science/pith/USKCBQLJZPNC57SEWD7LLC3PIU/action/author_attestation","sign_citation":"https://pith.science/pith/USKCBQLJZPNC57SEWD7LLC3PIU/action/citation_signature","submit_replication":"https://pith.science/pith/USKCBQLJZPNC57SEWD7LLC3PIU/action/replication_record"}},"created_at":"2026-07-05T04:56:12.651072+00:00","updated_at":"2026-07-05T04:56:12.651072+00:00"}