{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:DWTRBDQ3UKUFAQDGIM3SXQ7FHY","short_pith_number":"pith:DWTRBDQ3","schema_version":"1.0","canonical_sha256":"1da7108e1ba2a850406643372bc3e53e098d8878801c7b4eda5979f339c4fd64","source":{"kind":"arxiv","id":"1601.03778","version":2},"attestation_state":"computed","paper":{"title":"Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.LG","authors_text":"Baichuan Zhang, Khushbu Agarwal, Mohammad Al Hasan, Paola Pesntez Cabrera, Sumit Purohit, Sutanay Choudhury, Xia Ning","submitted_at":"2016-01-14T23:13:00Z","abstract_excerpt":"Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task. It differs from a traditional link prediction task in that the links in a knowledge graph are categorized into different predicates and the link prediction performance of different predicates in a knowledge graph generally varies widely. In this work, we propose a latent feature embedding based link prediction model which considers the prediction task for each predicate disjointly. To learn the model parameters it utilizes a Bayesian personalized ranking based optimi"},"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":"1601.03778","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-14T23:13:00Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"42d2088d92d28cb3460e359d886e204f9d34d203456b39b933f2e02cc1246dc2","abstract_canon_sha256":"31dc9ce4397c3abd27401b49e3d714cfd304bf1ed1235dc80c6de59ad132a6d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:50.041472Z","signature_b64":"VuZs8d9MyMdH9To9Z7q/jW6pGG3M47qKrLRSQh4UJx0H0p3mECxnALifQIM2YBwCZp8b4RNAJsE9Mk1PeKXyCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1da7108e1ba2a850406643372bc3e53e098d8878801c7b4eda5979f339c4fd64","last_reissued_at":"2026-05-18T01:20:50.041019Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:50.041019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.LG","authors_text":"Baichuan Zhang, Khushbu Agarwal, Mohammad Al Hasan, Paola Pesntez Cabrera, Sumit Purohit, Sutanay Choudhury, Xia Ning","submitted_at":"2016-01-14T23:13:00Z","abstract_excerpt":"Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task. It differs from a traditional link prediction task in that the links in a knowledge graph are categorized into different predicates and the link prediction performance of different predicates in a knowledge graph generally varies widely. In this work, we propose a latent feature embedding based link prediction model which considers the prediction task for each predicate disjointly. To learn the model parameters it utilizes a Bayesian personalized ranking based optimi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.03778","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":"1601.03778","created_at":"2026-05-18T01:20:50.041092+00:00"},{"alias_kind":"arxiv_version","alias_value":"1601.03778v2","created_at":"2026-05-18T01:20:50.041092+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.03778","created_at":"2026-05-18T01:20:50.041092+00:00"},{"alias_kind":"pith_short_12","alias_value":"DWTRBDQ3UKUF","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"DWTRBDQ3UKUFAQDG","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"DWTRBDQ3","created_at":"2026-05-18T12:30:12.583610+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/DWTRBDQ3UKUFAQDGIM3SXQ7FHY","json":"https://pith.science/pith/DWTRBDQ3UKUFAQDGIM3SXQ7FHY.json","graph_json":"https://pith.science/api/pith-number/DWTRBDQ3UKUFAQDGIM3SXQ7FHY/graph.json","events_json":"https://pith.science/api/pith-number/DWTRBDQ3UKUFAQDGIM3SXQ7FHY/events.json","paper":"https://pith.science/paper/DWTRBDQ3"},"agent_actions":{"view_html":"https://pith.science/pith/DWTRBDQ3UKUFAQDGIM3SXQ7FHY","download_json":"https://pith.science/pith/DWTRBDQ3UKUFAQDGIM3SXQ7FHY.json","view_paper":"https://pith.science/paper/DWTRBDQ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1601.03778&json=true","fetch_graph":"https://pith.science/api/pith-number/DWTRBDQ3UKUFAQDGIM3SXQ7FHY/graph.json","fetch_events":"https://pith.science/api/pith-number/DWTRBDQ3UKUFAQDGIM3SXQ7FHY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DWTRBDQ3UKUFAQDGIM3SXQ7FHY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DWTRBDQ3UKUFAQDGIM3SXQ7FHY/action/storage_attestation","attest_author":"https://pith.science/pith/DWTRBDQ3UKUFAQDGIM3SXQ7FHY/action/author_attestation","sign_citation":"https://pith.science/pith/DWTRBDQ3UKUFAQDGIM3SXQ7FHY/action/citation_signature","submit_replication":"https://pith.science/pith/DWTRBDQ3UKUFAQDGIM3SXQ7FHY/action/replication_record"}},"created_at":"2026-05-18T01:20:50.041092+00:00","updated_at":"2026-05-18T01:20:50.041092+00:00"}