{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VZBJIAUSLCZBR7C2UZYD3WBWXV","short_pith_number":"pith:VZBJIAUS","schema_version":"1.0","canonical_sha256":"ae4294029258b218fc5aa6703dd836bd4039e50f3428d4d1ce97542093c34391","source":{"kind":"arxiv","id":"2310.15797","version":1},"attestation_state":"computed","paper":{"title":"Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Jiaang Li, Licheng Zhang, Quan Wang, Yi Liu, Zhendong Mao","submitted_at":"2023-10-24T12:48:52Z","abstract_excerpt":"Representation Learning on Knowledge Graphs (KGs) is essential for downstream tasks. The dominant approach, KG Embedding (KGE), represents entities with independent vectors and faces the scalability challenge. Recent studies propose an alternative way for parameter efficiency, which represents entities by composing entity-corresponding codewords matched from predefined small-scale codebooks. We refer to the process of obtaining corresponding codewords of each entity as entity quantization, for which previous works have designed complicated strategies. Surprisingly, this paper shows that simple"},"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":"2310.15797","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-10-24T12:48:52Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"aafeb79e916aabcfa0496106a5c0c38f7e3ef3e3ba4d2a7f8ab6e46bf5a7dfdb","abstract_canon_sha256":"eb71e385d952507726794dbeb7b62fecfaa26755b3c84ab3a8b050a24fee3a72"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:04:31.077304Z","signature_b64":"BacmyTcuJPu0PcWK6zPYxIoBCSkSKmi2Qci5lr6/LRIW9tQ0AWR9jjxR0uQQqaAd4RxxiiXBL2hnXnUf3t3cDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae4294029258b218fc5aa6703dd836bd4039e50f3428d4d1ce97542093c34391","last_reissued_at":"2026-07-05T07:04:31.076891Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:04:31.076891Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Jiaang Li, Licheng Zhang, Quan Wang, Yi Liu, Zhendong Mao","submitted_at":"2023-10-24T12:48:52Z","abstract_excerpt":"Representation Learning on Knowledge Graphs (KGs) is essential for downstream tasks. The dominant approach, KG Embedding (KGE), represents entities with independent vectors and faces the scalability challenge. Recent studies propose an alternative way for parameter efficiency, which represents entities by composing entity-corresponding codewords matched from predefined small-scale codebooks. We refer to the process of obtaining corresponding codewords of each entity as entity quantization, for which previous works have designed complicated strategies. Surprisingly, this paper shows that simple"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.15797","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/2310.15797/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":"2310.15797","created_at":"2026-07-05T07:04:31.076953+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.15797v1","created_at":"2026-07-05T07:04:31.076953+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.15797","created_at":"2026-07-05T07:04:31.076953+00:00"},{"alias_kind":"pith_short_12","alias_value":"VZBJIAUSLCZB","created_at":"2026-07-05T07:04:31.076953+00:00"},{"alias_kind":"pith_short_16","alias_value":"VZBJIAUSLCZBR7C2","created_at":"2026-07-05T07:04:31.076953+00:00"},{"alias_kind":"pith_short_8","alias_value":"VZBJIAUS","created_at":"2026-07-05T07:04:31.076953+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/VZBJIAUSLCZBR7C2UZYD3WBWXV","json":"https://pith.science/pith/VZBJIAUSLCZBR7C2UZYD3WBWXV.json","graph_json":"https://pith.science/api/pith-number/VZBJIAUSLCZBR7C2UZYD3WBWXV/graph.json","events_json":"https://pith.science/api/pith-number/VZBJIAUSLCZBR7C2UZYD3WBWXV/events.json","paper":"https://pith.science/paper/VZBJIAUS"},"agent_actions":{"view_html":"https://pith.science/pith/VZBJIAUSLCZBR7C2UZYD3WBWXV","download_json":"https://pith.science/pith/VZBJIAUSLCZBR7C2UZYD3WBWXV.json","view_paper":"https://pith.science/paper/VZBJIAUS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.15797&json=true","fetch_graph":"https://pith.science/api/pith-number/VZBJIAUSLCZBR7C2UZYD3WBWXV/graph.json","fetch_events":"https://pith.science/api/pith-number/VZBJIAUSLCZBR7C2UZYD3WBWXV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VZBJIAUSLCZBR7C2UZYD3WBWXV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VZBJIAUSLCZBR7C2UZYD3WBWXV/action/storage_attestation","attest_author":"https://pith.science/pith/VZBJIAUSLCZBR7C2UZYD3WBWXV/action/author_attestation","sign_citation":"https://pith.science/pith/VZBJIAUSLCZBR7C2UZYD3WBWXV/action/citation_signature","submit_replication":"https://pith.science/pith/VZBJIAUSLCZBR7C2UZYD3WBWXV/action/replication_record"}},"created_at":"2026-07-05T07:04:31.076953+00:00","updated_at":"2026-07-05T07:04:31.076953+00:00"}