{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2LM5SSDEWDDB56RY5CB2QQIC74","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":"ed14f6762e14b1beb199e1bec08f7003fba117d8e345f1338669084615e614c9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-17T11:18:08Z","title_canon_sha256":"e97c16ea81ffb2780434307d2d5dd95dafdd69c8c7a0f8596d47d1eb77765804"},"schema_version":"1.0","source":{"id":"2507.13001","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.13001","created_at":"2026-07-05T11:38:43Z"},{"alias_kind":"arxiv_version","alias_value":"2507.13001v1","created_at":"2026-07-05T11:38:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.13001","created_at":"2026-07-05T11:38:43Z"},{"alias_kind":"pith_short_12","alias_value":"2LM5SSDEWDDB","created_at":"2026-07-05T11:38:43Z"},{"alias_kind":"pith_short_16","alias_value":"2LM5SSDEWDDB56RY","created_at":"2026-07-05T11:38:43Z"},{"alias_kind":"pith_short_8","alias_value":"2LM5SSDE","created_at":"2026-07-05T11:38:43Z"}],"graph_snapshots":[{"event_id":"sha256:85ba369797ec0bbcccb0da07d5133ae465650dbcbac6f607f9ed1aa3570ac95c","target":"graph","created_at":"2026-07-05T11:38:43Z","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/2507.13001/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Knowledge graph representation learning approaches provide a mapping between symbolic knowledge in the form of triples in a knowledge graph (KG) and their feature vectors. Knowledge graph embedding (KGE) models often represent relations in a KG as geometric transformations. Most state-of-the-art (SOTA) KGE models are derived from elementary geometric transformations (EGTs), such as translation, scaling, rotation, and reflection, or their combinations. These geometric transformations enable the models to effectively preserve specific structural and relational patterns of the KG. However, the cu","authors_text":"Andrea Coletta, Bowen Song, Jens Lehmann, Kossi Amouzouvi, Luigi Bellomarini, Sahar Vahdati","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-17T11:18:08Z","title":"SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.13001","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":"sha256:7f0838afee38ad9b99b96cfbc28dcba78a70f4735fea9e49ebb75152a4223305","target":"record","created_at":"2026-07-05T11:38:43Z","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":"ed14f6762e14b1beb199e1bec08f7003fba117d8e345f1338669084615e614c9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-17T11:18:08Z","title_canon_sha256":"e97c16ea81ffb2780434307d2d5dd95dafdd69c8c7a0f8596d47d1eb77765804"},"schema_version":"1.0","source":{"id":"2507.13001","kind":"arxiv","version":1}},"canonical_sha256":"d2d9d94864b0c61efa38e883a84102ff2e3ebdea31f0aaf64d0ce6e49d869aaa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d2d9d94864b0c61efa38e883a84102ff2e3ebdea31f0aaf64d0ce6e49d869aaa","first_computed_at":"2026-07-05T11:38:43.022564Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:38:43.022564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KLn/WtrUFjJYtDebubFdzMdmChqYvi/2iqkhSLq+AOaIws6MMi4Pwfz/PHnQYkwNoCP5jlVxCnE/vI4SI+Q5Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:38:43.023181Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.13001","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f0838afee38ad9b99b96cfbc28dcba78a70f4735fea9e49ebb75152a4223305","sha256:85ba369797ec0bbcccb0da07d5133ae465650dbcbac6f607f9ed1aa3570ac95c"],"state_sha256":"f9b0e9e1dd350b30217e3df8e57ee12d526570128adc83b7186802de8c7eb728"}