{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:U3GJHNLCBN7EP5WZPBAZNM63H6","short_pith_number":"pith:U3GJHNLC","schema_version":"1.0","canonical_sha256":"a6cc93b5620b7e47f6d9784196b3db3fafb74d3604c56e8eebe4b0d7ae4cb3af","source":{"kind":"arxiv","id":"2408.07611","version":2},"attestation_state":"computed","paper":{"title":"WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Hong Cheng, Kaihua Ni, Weijian Xie, Xuefeng Liang, Yuhui Liu, Zetian Hu","submitted_at":"2024-08-14T15:19:16Z","abstract_excerpt":"Large Language Models (LLMs) have greatly contributed to the development of adaptive intelligent agents and are positioned as an important way to achieve Artificial General Intelligence (AGI). However, LLMs are prone to produce factually incorrect information and often produce \"phantom\" content that undermines their reliability, which poses a serious challenge for their deployment in real-world scenarios. Enhancing LLMs by combining external databases and information retrieval mechanisms is an effective path. To address the above challenges, we propose a new approach called WeKnow-RAG, which i"},"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":"2408.07611","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-14T15:19:16Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"32b7a8018d7188c0f4709a7a98f751219ff3aedbc031abaa1ffb3fe5eeb5084b","abstract_canon_sha256":"18ed2507343acb7c9d3ced3fea5d8594b3a90616dcedde7b84d8131adf992af5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:07.135532Z","signature_b64":"x73N5LFdj9bcM+glA3iW5MaYxKE7xwK1PvxhMf2NHmSnWUUcOw4wITI2fVuxFKFx4PpN//KjSOZ/mXmdYR3qBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6cc93b5620b7e47f6d9784196b3db3fafb74d3604c56e8eebe4b0d7ae4cb3af","last_reissued_at":"2026-07-05T09:00:07.135039Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:07.135039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Hong Cheng, Kaihua Ni, Weijian Xie, Xuefeng Liang, Yuhui Liu, Zetian Hu","submitted_at":"2024-08-14T15:19:16Z","abstract_excerpt":"Large Language Models (LLMs) have greatly contributed to the development of adaptive intelligent agents and are positioned as an important way to achieve Artificial General Intelligence (AGI). However, LLMs are prone to produce factually incorrect information and often produce \"phantom\" content that undermines their reliability, which poses a serious challenge for their deployment in real-world scenarios. Enhancing LLMs by combining external databases and information retrieval mechanisms is an effective path. To address the above challenges, we propose a new approach called WeKnow-RAG, which i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.07611","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/2408.07611/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":"2408.07611","created_at":"2026-07-05T09:00:07.135097+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.07611v2","created_at":"2026-07-05T09:00:07.135097+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.07611","created_at":"2026-07-05T09:00:07.135097+00:00"},{"alias_kind":"pith_short_12","alias_value":"U3GJHNLCBN7E","created_at":"2026-07-05T09:00:07.135097+00:00"},{"alias_kind":"pith_short_16","alias_value":"U3GJHNLCBN7EP5WZ","created_at":"2026-07-05T09:00:07.135097+00:00"},{"alias_kind":"pith_short_8","alias_value":"U3GJHNLC","created_at":"2026-07-05T09:00:07.135097+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.17052","citing_title":"OASIS: On-Demand Hierarchical Event Memory for Streaming Video Reasoning","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17458","citing_title":"EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval","ref_index":232,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U3GJHNLCBN7EP5WZPBAZNM63H6","json":"https://pith.science/pith/U3GJHNLCBN7EP5WZPBAZNM63H6.json","graph_json":"https://pith.science/api/pith-number/U3GJHNLCBN7EP5WZPBAZNM63H6/graph.json","events_json":"https://pith.science/api/pith-number/U3GJHNLCBN7EP5WZPBAZNM63H6/events.json","paper":"https://pith.science/paper/U3GJHNLC"},"agent_actions":{"view_html":"https://pith.science/pith/U3GJHNLCBN7EP5WZPBAZNM63H6","download_json":"https://pith.science/pith/U3GJHNLCBN7EP5WZPBAZNM63H6.json","view_paper":"https://pith.science/paper/U3GJHNLC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.07611&json=true","fetch_graph":"https://pith.science/api/pith-number/U3GJHNLCBN7EP5WZPBAZNM63H6/graph.json","fetch_events":"https://pith.science/api/pith-number/U3GJHNLCBN7EP5WZPBAZNM63H6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U3GJHNLCBN7EP5WZPBAZNM63H6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U3GJHNLCBN7EP5WZPBAZNM63H6/action/storage_attestation","attest_author":"https://pith.science/pith/U3GJHNLCBN7EP5WZPBAZNM63H6/action/author_attestation","sign_citation":"https://pith.science/pith/U3GJHNLCBN7EP5WZPBAZNM63H6/action/citation_signature","submit_replication":"https://pith.science/pith/U3GJHNLCBN7EP5WZPBAZNM63H6/action/replication_record"}},"created_at":"2026-07-05T09:00:07.135097+00:00","updated_at":"2026-07-05T09:00:07.135097+00:00"}