{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:Y33JHRI52JQBQH5ZHWFWTKHM7W","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5de0db64a430713d7153cae49398cd742d7e61ab4c4622869e378fbcc401fa67","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2023-11-27T12:54:47Z","title_canon_sha256":"a13772cfbe8729fde37ac0f400df89fc84fe5f703b4fb50e5c39c9d371c32637"},"schema_version":"1.0","source":{"id":"2311.15781","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.15781","created_at":"2026-07-05T07:17:09Z"},{"alias_kind":"arxiv_version","alias_value":"2311.15781v1","created_at":"2026-07-05T07:17:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.15781","created_at":"2026-07-05T07:17:09Z"},{"alias_kind":"pith_short_12","alias_value":"Y33JHRI52JQB","created_at":"2026-07-05T07:17:09Z"},{"alias_kind":"pith_short_16","alias_value":"Y33JHRI52JQBQH5Z","created_at":"2026-07-05T07:17:09Z"},{"alias_kind":"pith_short_8","alias_value":"Y33JHRI5","created_at":"2026-07-05T07:17:09Z"}],"graph_snapshots":[{"event_id":"sha256:bd46a57d18769aa85992175550a8ccc7accd00c9e2dc39689333ab3580c4db35","target":"graph","created_at":"2026-07-05T07:17:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2311.15781/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent work in Natural Language Processing and Computer Vision has been using textual information -- e.g., entity names and descriptions -- available in knowledge graphs to ground neural models to high-quality structured data. However, when it comes to non-English languages, the quantity and quality of textual information are comparatively scarce. To address this issue, we introduce the novel task of automatic Knowledge Graph Enhancement (KGE) and perform a thorough investigation on bridging the gap in both the quantity and quality of textual information between English and non-English languag","authors_text":"Daniel Lee, Ihab Ilyas, Min Li, Simone Conia, Umar Farooq Minhas, Yunyao Li","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2023-11-27T12:54:47Z","title":"Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.15781","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b7c8afe646db92bad7c2d76fc492f94b646651a3b7d1b66626aa4a3731e5884e","target":"record","created_at":"2026-07-05T07:17:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5de0db64a430713d7153cae49398cd742d7e61ab4c4622869e378fbcc401fa67","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2023-11-27T12:54:47Z","title_canon_sha256":"a13772cfbe8729fde37ac0f400df89fc84fe5f703b4fb50e5c39c9d371c32637"},"schema_version":"1.0","source":{"id":"2311.15781","kind":"arxiv","version":1}},"canonical_sha256":"c6f693c51dd260181fb93d8b69a8ecfda4fff4ae2ba8f189317663abdb73aa0c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c6f693c51dd260181fb93d8b69a8ecfda4fff4ae2ba8f189317663abdb73aa0c","first_computed_at":"2026-07-05T07:17:09.348922Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:17:09.348922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p5n17C3N+CVu9n2r7d+VwFQu9fT8yZMr4SY2W0L1HY2bhjC8g5ROxv1M8mllZOSU24uMZUJWPQGpyYREkbMLDw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:17:09.349464Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.15781","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b7c8afe646db92bad7c2d76fc492f94b646651a3b7d1b66626aa4a3731e5884e","sha256:bd46a57d18769aa85992175550a8ccc7accd00c9e2dc39689333ab3580c4db35"],"state_sha256":"1bc0fe248b684ef09adaffeb66a68ab0335e6f572fb52bdb4e5479fdcec0a4a0"}