{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:I74YQ7BJO5P25LFUKV3C4ZFI36","short_pith_number":"pith:I74YQ7BJ","schema_version":"1.0","canonical_sha256":"47f9887c29775faeacb455762e64a8df96a241d00ba143373be59f30e4b3bc92","source":{"kind":"arxiv","id":"2112.02732","version":2},"attestation_state":"computed","paper":{"title":"JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Le Qi, Qi Shi, Yueqing Sun, Yu Zhang","submitted_at":"2021-12-06T01:46:46Z","abstract_excerpt":"Existing KG-augmented models for commonsense question answering primarily focus on designing elaborate Graph Neural Networks (GNNs) to model knowledge graphs (KGs). However, they ignore (i) the effectively fusing and reasoning over question context representations and the KG representations, and (ii) automatically selecting relevant nodes from the noisy KGs during reasoning. In this paper, we propose a novel model, JointLK, which solves the above limitations through the joint reasoning of LM and GNN and the dynamic KGs pruning mechanism. Specifically, JointLK performs joint reasoning between L"},"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":"2112.02732","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-12-06T01:46:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"fc7245ff8f9861ac090c59192f4441f20263fb61619b28952ddbb67ff928647b","abstract_canon_sha256":"ecafdf8b4526b49c77b796f187d24e234cdac0c0b3a6d38ec30c079d0e182867"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:19:04.471826Z","signature_b64":"IDB7PaR6tgtUn3i6YgkWqXQeKWFl6BptxUV7Ucwvb+sWjLL86/VPXBHfzgE2bhByIwJUMLJJx8UVTFs6vuwZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47f9887c29775faeacb455762e64a8df96a241d00ba143373be59f30e4b3bc92","last_reissued_at":"2026-07-05T04:19:04.471359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:19:04.471359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Le Qi, Qi Shi, Yueqing Sun, Yu Zhang","submitted_at":"2021-12-06T01:46:46Z","abstract_excerpt":"Existing KG-augmented models for commonsense question answering primarily focus on designing elaborate Graph Neural Networks (GNNs) to model knowledge graphs (KGs). However, they ignore (i) the effectively fusing and reasoning over question context representations and the KG representations, and (ii) automatically selecting relevant nodes from the noisy KGs during reasoning. In this paper, we propose a novel model, JointLK, which solves the above limitations through the joint reasoning of LM and GNN and the dynamic KGs pruning mechanism. Specifically, JointLK performs joint reasoning between L"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02732","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/2112.02732/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":"2112.02732","created_at":"2026-07-05T04:19:04.471416+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.02732v2","created_at":"2026-07-05T04:19:04.471416+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02732","created_at":"2026-07-05T04:19:04.471416+00:00"},{"alias_kind":"pith_short_12","alias_value":"I74YQ7BJO5P2","created_at":"2026-07-05T04:19:04.471416+00:00"},{"alias_kind":"pith_short_16","alias_value":"I74YQ7BJO5P25LFU","created_at":"2026-07-05T04:19:04.471416+00:00"},{"alias_kind":"pith_short_8","alias_value":"I74YQ7BJ","created_at":"2026-07-05T04:19:04.471416+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02452","citing_title":"Position: How can Graphs Help Large Language Models?","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I74YQ7BJO5P25LFUKV3C4ZFI36","json":"https://pith.science/pith/I74YQ7BJO5P25LFUKV3C4ZFI36.json","graph_json":"https://pith.science/api/pith-number/I74YQ7BJO5P25LFUKV3C4ZFI36/graph.json","events_json":"https://pith.science/api/pith-number/I74YQ7BJO5P25LFUKV3C4ZFI36/events.json","paper":"https://pith.science/paper/I74YQ7BJ"},"agent_actions":{"view_html":"https://pith.science/pith/I74YQ7BJO5P25LFUKV3C4ZFI36","download_json":"https://pith.science/pith/I74YQ7BJO5P25LFUKV3C4ZFI36.json","view_paper":"https://pith.science/paper/I74YQ7BJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.02732&json=true","fetch_graph":"https://pith.science/api/pith-number/I74YQ7BJO5P25LFUKV3C4ZFI36/graph.json","fetch_events":"https://pith.science/api/pith-number/I74YQ7BJO5P25LFUKV3C4ZFI36/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I74YQ7BJO5P25LFUKV3C4ZFI36/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I74YQ7BJO5P25LFUKV3C4ZFI36/action/storage_attestation","attest_author":"https://pith.science/pith/I74YQ7BJO5P25LFUKV3C4ZFI36/action/author_attestation","sign_citation":"https://pith.science/pith/I74YQ7BJO5P25LFUKV3C4ZFI36/action/citation_signature","submit_replication":"https://pith.science/pith/I74YQ7BJO5P25LFUKV3C4ZFI36/action/replication_record"}},"created_at":"2026-07-05T04:19:04.471416+00:00","updated_at":"2026-07-05T04:19:04.471416+00:00"}