{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZZHKPPZZUZGSOIMSER3NHA3FQF","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":"2058d93fbcb9f6dabdcfff55470e94fb3bc444d6b39903aa9e39310ac7d5ade9","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-31T12:41:02Z","title_canon_sha256":"f7fdff4c69e4f18388994fe22f92ab72322bb2c756ff94b8a2b70e5bc45f4e17"},"schema_version":"1.0","source":{"id":"2501.19095","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.19095","created_at":"2026-07-05T10:07:56Z"},{"alias_kind":"arxiv_version","alias_value":"2501.19095v1","created_at":"2026-07-05T10:07:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.19095","created_at":"2026-07-05T10:07:56Z"},{"alias_kind":"pith_short_12","alias_value":"ZZHKPPZZUZGS","created_at":"2026-07-05T10:07:56Z"},{"alias_kind":"pith_short_16","alias_value":"ZZHKPPZZUZGSOIMS","created_at":"2026-07-05T10:07:56Z"},{"alias_kind":"pith_short_8","alias_value":"ZZHKPPZZ","created_at":"2026-07-05T10:07:56Z"}],"graph_snapshots":[{"event_id":"sha256:0eb3923a26aa87f7f33d98fddc0684c2419c1fbf3a21f886455e657f04aebbb5","target":"graph","created_at":"2026-07-05T10:07:56Z","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/2501.19095/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Knowledge Graphs (KGs) store human knowledge in the form of entities (nodes) and relations, and are used extensively in various applications. KG embeddings are an effective approach to addressing tasks like knowledge discovery, link prediction, and reasoning. This is often done by allocating and learning embedding tables for all or a subset of the entities. As this scales linearly with the number of entities, learning embedding models in real-world KGs with millions of nodes can be computationally intractable. To address this scalability problem, our model, PathE, only allocates embedding tabl","authors_text":"Albert Mero\\~no-Pe\\~nuela, Elena Simperl, Ioannis Reklos, Jacopo de Berardinis","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-31T12:41:02Z","title":"PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.19095","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:fbcad25667e04c9cb3c70949a04c4c845baaaaafdec7568ad5f5cba1d6db9739","target":"record","created_at":"2026-07-05T10:07:56Z","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":"2058d93fbcb9f6dabdcfff55470e94fb3bc444d6b39903aa9e39310ac7d5ade9","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-31T12:41:02Z","title_canon_sha256":"f7fdff4c69e4f18388994fe22f92ab72322bb2c756ff94b8a2b70e5bc45f4e17"},"schema_version":"1.0","source":{"id":"2501.19095","kind":"arxiv","version":1}},"canonical_sha256":"ce4ea7bf39a64d2721922476d383658146059d675f64176181daccb18545c549","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce4ea7bf39a64d2721922476d383658146059d675f64176181daccb18545c549","first_computed_at":"2026-07-05T10:07:56.098844Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:07:56.098844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TEIAw9xpQLwP6oexvkCyrYRMQ4ujairEOxYpB5uo3QeHk5wnsoS+bAAo1crf3PfUAvCrTdEV/zFTRFcOzJgVAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:07:56.099385Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.19095","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbcad25667e04c9cb3c70949a04c4c845baaaaafdec7568ad5f5cba1d6db9739","sha256:0eb3923a26aa87f7f33d98fddc0684c2419c1fbf3a21f886455e657f04aebbb5"],"state_sha256":"8c2cba6116b176076d8bc1bbac5fb9b3f23f3a29353475d91a1d5b8489120b09"}