{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JR2M45GAYRBEGHBUZPDIOSYVIA","short_pith_number":"pith:JR2M45GA","schema_version":"1.0","canonical_sha256":"4c74ce74c0c442431c34cbc6874b1540367311b5bbd4e3ebf3da52fcc8159ff2","source":{"kind":"arxiv","id":"1803.07828","version":2},"attestation_state":"computed","paper":{"title":"Expeditious Generation of Knowledge Graph Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alexander Bigerl, Andr\\'e Valdestilhas, Diego Esteves, Diego Moussallem, Edgard Marx, Stefano Ruberto, Tommaso Soru","submitted_at":"2018-03-21T10:06:28Z","abstract_excerpt":"Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddings have shown promising results on tasks such as link prediction, entity recommendation, question answering, and triplet classification. However, only a few methods can compute low-dimensional embeddings of very large knowledge bases without needing state-of-the-art computational resources. In this paper, we propose KG2Vec, a simple and fast approach to Knowledge Graph Embedding based on the skip-gram model. Instead"},"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":"1803.07828","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-03-21T10:06:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0961d01dd56d063df655a6097590bcd087428511a98190c4fe0f20970a0b444b","abstract_canon_sha256":"afcd43437346495605c437479a1e2f479bf08dcdb7849bb54a7a82eb051780e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:15.091372Z","signature_b64":"5kGNXlJWrEKX4sLZD2FLX7zptqKgTs3i3rtO0uixVOmRRZ742SUnxUJC/wC3syxT7ckhw2lgDKqnXmt7WTikAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c74ce74c0c442431c34cbc6874b1540367311b5bbd4e3ebf3da52fcc8159ff2","last_reissued_at":"2026-05-18T00:01:15.090909Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:15.090909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Expeditious Generation of Knowledge Graph Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alexander Bigerl, Andr\\'e Valdestilhas, Diego Esteves, Diego Moussallem, Edgard Marx, Stefano Ruberto, Tommaso Soru","submitted_at":"2018-03-21T10:06:28Z","abstract_excerpt":"Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddings have shown promising results on tasks such as link prediction, entity recommendation, question answering, and triplet classification. However, only a few methods can compute low-dimensional embeddings of very large knowledge bases without needing state-of-the-art computational resources. In this paper, we propose KG2Vec, a simple and fast approach to Knowledge Graph Embedding based on the skip-gram model. Instead"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07828","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1803.07828","created_at":"2026-05-18T00:01:15.090980+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.07828v2","created_at":"2026-05-18T00:01:15.090980+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07828","created_at":"2026-05-18T00:01:15.090980+00:00"},{"alias_kind":"pith_short_12","alias_value":"JR2M45GAYRBE","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"JR2M45GAYRBEGHBU","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"JR2M45GA","created_at":"2026-05-18T12:32:31.084164+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/JR2M45GAYRBEGHBUZPDIOSYVIA","json":"https://pith.science/pith/JR2M45GAYRBEGHBUZPDIOSYVIA.json","graph_json":"https://pith.science/api/pith-number/JR2M45GAYRBEGHBUZPDIOSYVIA/graph.json","events_json":"https://pith.science/api/pith-number/JR2M45GAYRBEGHBUZPDIOSYVIA/events.json","paper":"https://pith.science/paper/JR2M45GA"},"agent_actions":{"view_html":"https://pith.science/pith/JR2M45GAYRBEGHBUZPDIOSYVIA","download_json":"https://pith.science/pith/JR2M45GAYRBEGHBUZPDIOSYVIA.json","view_paper":"https://pith.science/paper/JR2M45GA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.07828&json=true","fetch_graph":"https://pith.science/api/pith-number/JR2M45GAYRBEGHBUZPDIOSYVIA/graph.json","fetch_events":"https://pith.science/api/pith-number/JR2M45GAYRBEGHBUZPDIOSYVIA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JR2M45GAYRBEGHBUZPDIOSYVIA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JR2M45GAYRBEGHBUZPDIOSYVIA/action/storage_attestation","attest_author":"https://pith.science/pith/JR2M45GAYRBEGHBUZPDIOSYVIA/action/author_attestation","sign_citation":"https://pith.science/pith/JR2M45GAYRBEGHBUZPDIOSYVIA/action/citation_signature","submit_replication":"https://pith.science/pith/JR2M45GAYRBEGHBUZPDIOSYVIA/action/replication_record"}},"created_at":"2026-05-18T00:01:15.090980+00:00","updated_at":"2026-05-18T00:01:15.090980+00:00"}