{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:C3ZZB5AB2X3R6PS7DX7G4VUUXK","short_pith_number":"pith:C3ZZB5AB","schema_version":"1.0","canonical_sha256":"16f390f401d5f71f3e5f1dfe6e5694ba9233c72577d573bfe944078372d3c186","source":{"kind":"arxiv","id":"2503.02172","version":1},"attestation_state":"computed","paper":{"title":"KGCompiler: Deep Learning Compilation Optimization for Knowledge Graph Complex Logical Query Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.AI","authors_text":"Hanghang Cao, Haoran Luo, Hongyu Lin, Kaichun Yao, Libo Zhang, Mingjie Xing, Shihao Gao, Yang Liu, Yanjun Wu","submitted_at":"2025-03-04T01:24:32Z","abstract_excerpt":"Complex Logical Query Answering (CLQA) involves intricate multi-hop logical reasoning over large-scale and potentially incomplete Knowledge Graphs (KGs). Although existing CLQA algorithms achieve high accuracy in answering such queries, their reasoning time and memory usage scale significantly with the number of First-Order Logic (FOL) operators involved, creating serious challenges for practical deployment. In addition, current research primarily focuses on algorithm-level optimizations for CLQA tasks, often overlooking compiler-level optimizations, which can offer greater generality and scal"},"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":"2503.02172","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-03-04T01:24:32Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"c62fcdd3d0729dbfe64bab6630c8819c4e44aa4077a34db5b60e061d62307394","abstract_canon_sha256":"cbb39bb1d3f9814875a1476c78a30ffc84d5d31d0f0b6eb9894b2adf097da5c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:23:38.582036Z","signature_b64":"33rnDvKtMkhCaZTF97YaTEsf6Ft//vlIpmk3vfZYq2GiHrZiBvO0GBUeh6YyDeXSFM8E6btxj1FZyDcLSN8lCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16f390f401d5f71f3e5f1dfe6e5694ba9233c72577d573bfe944078372d3c186","last_reissued_at":"2026-07-05T10:23:38.581448Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:23:38.581448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KGCompiler: Deep Learning Compilation Optimization for Knowledge Graph Complex Logical Query Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.AI","authors_text":"Hanghang Cao, Haoran Luo, Hongyu Lin, Kaichun Yao, Libo Zhang, Mingjie Xing, Shihao Gao, Yang Liu, Yanjun Wu","submitted_at":"2025-03-04T01:24:32Z","abstract_excerpt":"Complex Logical Query Answering (CLQA) involves intricate multi-hop logical reasoning over large-scale and potentially incomplete Knowledge Graphs (KGs). Although existing CLQA algorithms achieve high accuracy in answering such queries, their reasoning time and memory usage scale significantly with the number of First-Order Logic (FOL) operators involved, creating serious challenges for practical deployment. In addition, current research primarily focuses on algorithm-level optimizations for CLQA tasks, often overlooking compiler-level optimizations, which can offer greater generality and scal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02172","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/2503.02172/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":"2503.02172","created_at":"2026-07-05T10:23:38.581516+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.02172v1","created_at":"2026-07-05T10:23:38.581516+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02172","created_at":"2026-07-05T10:23:38.581516+00:00"},{"alias_kind":"pith_short_12","alias_value":"C3ZZB5AB2X3R","created_at":"2026-07-05T10:23:38.581516+00:00"},{"alias_kind":"pith_short_16","alias_value":"C3ZZB5AB2X3R6PS7","created_at":"2026-07-05T10:23:38.581516+00:00"},{"alias_kind":"pith_short_8","alias_value":"C3ZZB5AB","created_at":"2026-07-05T10:23:38.581516+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/C3ZZB5AB2X3R6PS7DX7G4VUUXK","json":"https://pith.science/pith/C3ZZB5AB2X3R6PS7DX7G4VUUXK.json","graph_json":"https://pith.science/api/pith-number/C3ZZB5AB2X3R6PS7DX7G4VUUXK/graph.json","events_json":"https://pith.science/api/pith-number/C3ZZB5AB2X3R6PS7DX7G4VUUXK/events.json","paper":"https://pith.science/paper/C3ZZB5AB"},"agent_actions":{"view_html":"https://pith.science/pith/C3ZZB5AB2X3R6PS7DX7G4VUUXK","download_json":"https://pith.science/pith/C3ZZB5AB2X3R6PS7DX7G4VUUXK.json","view_paper":"https://pith.science/paper/C3ZZB5AB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.02172&json=true","fetch_graph":"https://pith.science/api/pith-number/C3ZZB5AB2X3R6PS7DX7G4VUUXK/graph.json","fetch_events":"https://pith.science/api/pith-number/C3ZZB5AB2X3R6PS7DX7G4VUUXK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C3ZZB5AB2X3R6PS7DX7G4VUUXK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C3ZZB5AB2X3R6PS7DX7G4VUUXK/action/storage_attestation","attest_author":"https://pith.science/pith/C3ZZB5AB2X3R6PS7DX7G4VUUXK/action/author_attestation","sign_citation":"https://pith.science/pith/C3ZZB5AB2X3R6PS7DX7G4VUUXK/action/citation_signature","submit_replication":"https://pith.science/pith/C3ZZB5AB2X3R6PS7DX7G4VUUXK/action/replication_record"}},"created_at":"2026-07-05T10:23:38.581516+00:00","updated_at":"2026-07-05T10:23:38.581516+00:00"}