{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:2XQDGPMHD6EOOCFCVD3GQQ27PE","short_pith_number":"pith:2XQDGPMH","schema_version":"1.0","canonical_sha256":"d5e0333d871f88e708a2a8f668435f791086b4957601193eeeb03882f6a3eb51","source":{"kind":"arxiv","id":"1906.05489","version":1},"attestation_state":"computed","paper":{"title":"Cognitive Knowledge Graph Reasoning for One-shot Relational Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chang Zhou, Hongxia Yang, Jie Tang, Ming Ding, Zhengxiao Du","submitted_at":"2019-06-13T05:39:42Z","abstract_excerpt":"Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently. However, few studies have focused on relation types unseen in the original KG, given only one or a few instances for training. To bridge this gap, we propose CogKR for one-shot KG reasoning. The one-shot relational learning problem is tackled through two modules: the summary module summarizes the underlying relationship of the given instances, based on which the reasoning module infers the correct answers. Motivated by the dual process "},"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":"1906.05489","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-13T05:39:42Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"57d64bc1810e54866eaceebda617af750c4c10fe7ee23b3ce90f4a61fa45109c","abstract_canon_sha256":"fd87e89aee54135ab752fefa34aecef9d63ed6b5232bbe591acfd3978f729c93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:25.234420Z","signature_b64":"wNwDYbb/tmdYmCfrsoOnzjMP041Mp1IfxB4yeGv1LwFmhJPk0MNwlxXeG6EECE0agOKDqtlxrw6VSLjEvVL5Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5e0333d871f88e708a2a8f668435f791086b4957601193eeeb03882f6a3eb51","last_reissued_at":"2026-05-17T23:43:25.233965Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:25.233965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cognitive Knowledge Graph Reasoning for One-shot Relational Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chang Zhou, Hongxia Yang, Jie Tang, Ming Ding, Zhengxiao Du","submitted_at":"2019-06-13T05:39:42Z","abstract_excerpt":"Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently. However, few studies have focused on relation types unseen in the original KG, given only one or a few instances for training. To bridge this gap, we propose CogKR for one-shot KG reasoning. The one-shot relational learning problem is tackled through two modules: the summary module summarizes the underlying relationship of the given instances, based on which the reasoning module infers the correct answers. Motivated by the dual process "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.05489","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":""},"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":"1906.05489","created_at":"2026-05-17T23:43:25.234027+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.05489v1","created_at":"2026-05-17T23:43:25.234027+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.05489","created_at":"2026-05-17T23:43:25.234027+00:00"},{"alias_kind":"pith_short_12","alias_value":"2XQDGPMHD6EO","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"2XQDGPMHD6EOOCFC","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"2XQDGPMH","created_at":"2026-05-18T12:33:07.085635+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/2XQDGPMHD6EOOCFCVD3GQQ27PE","json":"https://pith.science/pith/2XQDGPMHD6EOOCFCVD3GQQ27PE.json","graph_json":"https://pith.science/api/pith-number/2XQDGPMHD6EOOCFCVD3GQQ27PE/graph.json","events_json":"https://pith.science/api/pith-number/2XQDGPMHD6EOOCFCVD3GQQ27PE/events.json","paper":"https://pith.science/paper/2XQDGPMH"},"agent_actions":{"view_html":"https://pith.science/pith/2XQDGPMHD6EOOCFCVD3GQQ27PE","download_json":"https://pith.science/pith/2XQDGPMHD6EOOCFCVD3GQQ27PE.json","view_paper":"https://pith.science/paper/2XQDGPMH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.05489&json=true","fetch_graph":"https://pith.science/api/pith-number/2XQDGPMHD6EOOCFCVD3GQQ27PE/graph.json","fetch_events":"https://pith.science/api/pith-number/2XQDGPMHD6EOOCFCVD3GQQ27PE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2XQDGPMHD6EOOCFCVD3GQQ27PE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2XQDGPMHD6EOOCFCVD3GQQ27PE/action/storage_attestation","attest_author":"https://pith.science/pith/2XQDGPMHD6EOOCFCVD3GQQ27PE/action/author_attestation","sign_citation":"https://pith.science/pith/2XQDGPMHD6EOOCFCVD3GQQ27PE/action/citation_signature","submit_replication":"https://pith.science/pith/2XQDGPMHD6EOOCFCVD3GQQ27PE/action/replication_record"}},"created_at":"2026-05-17T23:43:25.234027+00:00","updated_at":"2026-05-17T23:43:25.234027+00:00"}