{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:BFCH7N2BYLVMLZSWRP7EK4MRY7","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":"6b0e346e22c3d74c2fb370e8f2d9608d061a6b9f002d13e9b0a0fcfe4e3411bc","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-28T12:54:28Z","title_canon_sha256":"ca7665acbd912fa6f6e0bea5a3b1b3f0c2ce66253122475bb1b6276ebc4da2d7"},"schema_version":"1.0","source":{"id":"2007.14175","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.14175","created_at":"2026-07-05T01:23:26Z"},{"alias_kind":"arxiv_version","alias_value":"2007.14175v2","created_at":"2026-07-05T01:23:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.14175","created_at":"2026-07-05T01:23:26Z"},{"alias_kind":"pith_short_12","alias_value":"BFCH7N2BYLVM","created_at":"2026-07-05T01:23:26Z"},{"alias_kind":"pith_short_16","alias_value":"BFCH7N2BYLVMLZSW","created_at":"2026-07-05T01:23:26Z"},{"alias_kind":"pith_short_8","alias_value":"BFCH7N2B","created_at":"2026-07-05T01:23:26Z"}],"graph_snapshots":[{"event_id":"sha256:f58fbe52039d82703fd06483ee3e7ed4d3079e1f27b6d7c645303c4ec8a48fa5","target":"graph","created_at":"2026-07-05T01:23:26Z","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/2007.14175/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them addresses specific needs, we re-designed and re-implemented PyKEEN, one of the first KGE libraries, in a community effort. PyKEEN 1.0 enables users to compose knowledge graph embedding models (KGEMs) based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. Besides, an automatic memory optimization has been realized in order to exploit th","authors_text":"Charles Tapley Hoyt, Jens Lehmann, Laurent Vermue, Max Berrendorf, Mehdi Ali, Sahand Sharifzadeh, Volker Tresp","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-28T12:54:28Z","title":"PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.14175","kind":"arxiv","version":2},"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:b63acaec57fc159fe188793644062bd350eaaf79f226d55d4c4dd8c1bb642952","target":"record","created_at":"2026-07-05T01:23:26Z","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":"6b0e346e22c3d74c2fb370e8f2d9608d061a6b9f002d13e9b0a0fcfe4e3411bc","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-28T12:54:28Z","title_canon_sha256":"ca7665acbd912fa6f6e0bea5a3b1b3f0c2ce66253122475bb1b6276ebc4da2d7"},"schema_version":"1.0","source":{"id":"2007.14175","kind":"arxiv","version":2}},"canonical_sha256":"09447fb741c2eac5e6568bfe457191c7f57521ca6af9d535470dc6dc0550c06e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"09447fb741c2eac5e6568bfe457191c7f57521ca6af9d535470dc6dc0550c06e","first_computed_at":"2026-07-05T01:23:26.173738Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:23:26.173738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"poP3bKooq0zCBjptjcMON7wOdBCe31OHsMQYFwzADII+wl0aVZC0UqY45wBSda0gPOoCGVUyN67XFqoOe2c8CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:23:26.174901Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.14175","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b63acaec57fc159fe188793644062bd350eaaf79f226d55d4c4dd8c1bb642952","sha256:f58fbe52039d82703fd06483ee3e7ed4d3079e1f27b6d7c645303c4ec8a48fa5"],"state_sha256":"812990901fb0244d64db4bc666a8781231f99fb38477d2974588d478c813c0b8"}