{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CQTHHWYEDUIHVVTGESSISIP4ZH","short_pith_number":"pith:CQTHHWYE","schema_version":"1.0","canonical_sha256":"142673db041d107ad66624a48921fcc9c24e397cc846249d008a53da4bfbb55d","source":{"kind":"arxiv","id":"2403.10110","version":1},"attestation_state":"computed","paper":{"title":"Meta Operator for Complex Query Answering on Knowledge Graphs","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LO"],"primary_cat":"cs.LG","authors_text":"Hang Yin, Yangqiu Song, Zihao Wang","submitted_at":"2024-03-15T08:54:25Z","abstract_excerpt":"Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (CQA) models are proposed to directly learn from the query-answer samples to avoid the direct traversal of incomplete graph data. Existing works formulate the training of complex query answering models as multi-task learning and require a large number of training samples. In this work, we explore the compositional structure of complex queries and argue that the different logical operator types, rather than the differe"},"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":"2403.10110","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-15T08:54:25Z","cross_cats_sorted":["cs.AI","cs.LO"],"title_canon_sha256":"05a6e1ae9297e44f7373734e2ac2f29571e89c4f60564bc619192906ee76e879","abstract_canon_sha256":"935f0df24634ba6fda9decd56051ab90a32723afc84eafe614fb66547febf33b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:30.467812Z","signature_b64":"KkToNub1x/RHe+a75M0YqMtFk6dCYwaN/2pFaAELOklvQYJuVMPpEh8eLzCNqdDgRynslHH/FsXpsKSTY9boCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"142673db041d107ad66624a48921fcc9c24e397cc846249d008a53da4bfbb55d","last_reissued_at":"2026-07-05T07:56:30.467335Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:30.467335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta Operator for Complex Query Answering on Knowledge Graphs","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LO"],"primary_cat":"cs.LG","authors_text":"Hang Yin, Yangqiu Song, Zihao Wang","submitted_at":"2024-03-15T08:54:25Z","abstract_excerpt":"Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (CQA) models are proposed to directly learn from the query-answer samples to avoid the direct traversal of incomplete graph data. Existing works formulate the training of complex query answering models as multi-task learning and require a large number of training samples. In this work, we explore the compositional structure of complex queries and argue that the different logical operator types, rather than the differe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.10110","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/2403.10110/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":"2403.10110","created_at":"2026-07-05T07:56:30.467392+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.10110v1","created_at":"2026-07-05T07:56:30.467392+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.10110","created_at":"2026-07-05T07:56:30.467392+00:00"},{"alias_kind":"pith_short_12","alias_value":"CQTHHWYEDUIH","created_at":"2026-07-05T07:56:30.467392+00:00"},{"alias_kind":"pith_short_16","alias_value":"CQTHHWYEDUIHVVTG","created_at":"2026-07-05T07:56:30.467392+00:00"},{"alias_kind":"pith_short_8","alias_value":"CQTHHWYE","created_at":"2026-07-05T07:56:30.467392+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25985","citing_title":"Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CQTHHWYEDUIHVVTGESSISIP4ZH","json":"https://pith.science/pith/CQTHHWYEDUIHVVTGESSISIP4ZH.json","graph_json":"https://pith.science/api/pith-number/CQTHHWYEDUIHVVTGESSISIP4ZH/graph.json","events_json":"https://pith.science/api/pith-number/CQTHHWYEDUIHVVTGESSISIP4ZH/events.json","paper":"https://pith.science/paper/CQTHHWYE"},"agent_actions":{"view_html":"https://pith.science/pith/CQTHHWYEDUIHVVTGESSISIP4ZH","download_json":"https://pith.science/pith/CQTHHWYEDUIHVVTGESSISIP4ZH.json","view_paper":"https://pith.science/paper/CQTHHWYE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.10110&json=true","fetch_graph":"https://pith.science/api/pith-number/CQTHHWYEDUIHVVTGESSISIP4ZH/graph.json","fetch_events":"https://pith.science/api/pith-number/CQTHHWYEDUIHVVTGESSISIP4ZH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CQTHHWYEDUIHVVTGESSISIP4ZH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CQTHHWYEDUIHVVTGESSISIP4ZH/action/storage_attestation","attest_author":"https://pith.science/pith/CQTHHWYEDUIHVVTGESSISIP4ZH/action/author_attestation","sign_citation":"https://pith.science/pith/CQTHHWYEDUIHVVTGESSISIP4ZH/action/citation_signature","submit_replication":"https://pith.science/pith/CQTHHWYEDUIHVVTGESSISIP4ZH/action/replication_record"}},"created_at":"2026-07-05T07:56:30.467392+00:00","updated_at":"2026-07-05T07:56:30.467392+00:00"}