{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:SKQPIGI2H5JX3WW57Y3T3T6NXP","short_pith_number":"pith:SKQPIGI2","schema_version":"1.0","canonical_sha256":"92a0f4191a3f537ddaddfe373dcfcdbbf2a3b117d93249b7e2655f74e36ef840","source":{"kind":"arxiv","id":"2007.11164","version":1},"attestation_state":"computed","paper":{"title":"Time-aware Graph Embedding: A temporal smoothness and task-oriented approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chuanyan Miao, Dong Yang, Hengjie Song, Ke Wang, Shengjie Sun, Xiaonan Meng, Yi Hu, Yonghui Xu, Yuan Miao","submitted_at":"2020-07-22T02:20:25Z","abstract_excerpt":"Knowledge graph embedding, which aims to learn the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge graph embedding methods focus on the structural relationships in fixed triples while ignoring the temporal information. Currently, existing time-aware graph embedding methods only focus on the factual plausibility, while ignoring the temporal smoothness which models the interactions between a fact and its contexts, and thus can capture fine-granularity temporal relationships. This leads to the limited per"},"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":"2007.11164","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-22T02:20:25Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7762e62cd6daac80264d0abf1afc82c53fb1c84ff1b3b0f6b00c69bcc7200b1f","abstract_canon_sha256":"4d8bbf2ab1f5f7124615f7396eed4a2733eccbe743b97972a8f0e619ebefb3b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:21:23.568175Z","signature_b64":"rIPnweU9T+7IBJMuuk6U1lYIjOO1JaoyUXcKDDBdL2miUbxarWF/DPxYALvCDGluHqBQXv8UMajy+iYaD3cFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92a0f4191a3f537ddaddfe373dcfcdbbf2a3b117d93249b7e2655f74e36ef840","last_reissued_at":"2026-07-05T01:21:23.567702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:21:23.567702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Time-aware Graph Embedding: A temporal smoothness and task-oriented approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chuanyan Miao, Dong Yang, Hengjie Song, Ke Wang, Shengjie Sun, Xiaonan Meng, Yi Hu, Yonghui Xu, Yuan Miao","submitted_at":"2020-07-22T02:20:25Z","abstract_excerpt":"Knowledge graph embedding, which aims to learn the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge graph embedding methods focus on the structural relationships in fixed triples while ignoring the temporal information. Currently, existing time-aware graph embedding methods only focus on the factual plausibility, while ignoring the temporal smoothness which models the interactions between a fact and its contexts, and thus can capture fine-granularity temporal relationships. This leads to the limited per"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.11164","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/2007.11164/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":"2007.11164","created_at":"2026-07-05T01:21:23.567764+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.11164v1","created_at":"2026-07-05T01:21:23.567764+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.11164","created_at":"2026-07-05T01:21:23.567764+00:00"},{"alias_kind":"pith_short_12","alias_value":"SKQPIGI2H5JX","created_at":"2026-07-05T01:21:23.567764+00:00"},{"alias_kind":"pith_short_16","alias_value":"SKQPIGI2H5JX3WW5","created_at":"2026-07-05T01:21:23.567764+00:00"},{"alias_kind":"pith_short_8","alias_value":"SKQPIGI2","created_at":"2026-07-05T01:21:23.567764+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/SKQPIGI2H5JX3WW57Y3T3T6NXP","json":"https://pith.science/pith/SKQPIGI2H5JX3WW57Y3T3T6NXP.json","graph_json":"https://pith.science/api/pith-number/SKQPIGI2H5JX3WW57Y3T3T6NXP/graph.json","events_json":"https://pith.science/api/pith-number/SKQPIGI2H5JX3WW57Y3T3T6NXP/events.json","paper":"https://pith.science/paper/SKQPIGI2"},"agent_actions":{"view_html":"https://pith.science/pith/SKQPIGI2H5JX3WW57Y3T3T6NXP","download_json":"https://pith.science/pith/SKQPIGI2H5JX3WW57Y3T3T6NXP.json","view_paper":"https://pith.science/paper/SKQPIGI2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.11164&json=true","fetch_graph":"https://pith.science/api/pith-number/SKQPIGI2H5JX3WW57Y3T3T6NXP/graph.json","fetch_events":"https://pith.science/api/pith-number/SKQPIGI2H5JX3WW57Y3T3T6NXP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SKQPIGI2H5JX3WW57Y3T3T6NXP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SKQPIGI2H5JX3WW57Y3T3T6NXP/action/storage_attestation","attest_author":"https://pith.science/pith/SKQPIGI2H5JX3WW57Y3T3T6NXP/action/author_attestation","sign_citation":"https://pith.science/pith/SKQPIGI2H5JX3WW57Y3T3T6NXP/action/citation_signature","submit_replication":"https://pith.science/pith/SKQPIGI2H5JX3WW57Y3T3T6NXP/action/replication_record"}},"created_at":"2026-07-05T01:21:23.567764+00:00","updated_at":"2026-07-05T01:21:23.567764+00:00"}