{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:HSOTSG6PY72ZNKM4PNX3WWQPZF","short_pith_number":"pith:HSOTSG6P","schema_version":"1.0","canonical_sha256":"3c9d391bcfc7f596a99c7b6fbb5a0fc9555750c912bd75a0cb8efd41d813c6f2","source":{"kind":"arxiv","id":"1903.04750","version":1},"attestation_state":"computed","paper":{"title":"Interaction Embeddings for Prediction and Explanation in Knowledge Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Abraham Bernstein, Bibek Paudel, Huajun Chen, Wei Zhang, Wen Zhang","submitted_at":"2019-03-12T07:12:46Z","abstract_excerpt":"Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific"},"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":"1903.04750","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-03-12T07:12:46Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"bde57319379ebcdca5c2520b15506ab5822b35352104c64d48aa9b5911b29c6b","abstract_canon_sha256":"e36880ebffaa84fd5dab145912e1f4a7e748f149ce4c63429c447e7ad70a1fbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:28.798340Z","signature_b64":"LGImmEj/VBY0rN6VGuPCEEQ2/lJBwt+HFpowzr1HY3TpfW4YPLdlPMBHELKYMCsXAYbRBl81RY41pHNi4ioCCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c9d391bcfc7f596a99c7b6fbb5a0fc9555750c912bd75a0cb8efd41d813c6f2","last_reissued_at":"2026-05-17T23:51:28.797871Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:28.797871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interaction Embeddings for Prediction and Explanation in Knowledge Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Abraham Bernstein, Bibek Paudel, Huajun Chen, Wei Zhang, Wen Zhang","submitted_at":"2019-03-12T07:12:46Z","abstract_excerpt":"Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.04750","kind":"arxiv","version":1},"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":"1903.04750","created_at":"2026-05-17T23:51:28.797933+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.04750v1","created_at":"2026-05-17T23:51:28.797933+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.04750","created_at":"2026-05-17T23:51:28.797933+00:00"},{"alias_kind":"pith_short_12","alias_value":"HSOTSG6PY72Z","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"HSOTSG6PY72ZNKM4","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"HSOTSG6P","created_at":"2026-05-18T12:33:18.533446+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/HSOTSG6PY72ZNKM4PNX3WWQPZF","json":"https://pith.science/pith/HSOTSG6PY72ZNKM4PNX3WWQPZF.json","graph_json":"https://pith.science/api/pith-number/HSOTSG6PY72ZNKM4PNX3WWQPZF/graph.json","events_json":"https://pith.science/api/pith-number/HSOTSG6PY72ZNKM4PNX3WWQPZF/events.json","paper":"https://pith.science/paper/HSOTSG6P"},"agent_actions":{"view_html":"https://pith.science/pith/HSOTSG6PY72ZNKM4PNX3WWQPZF","download_json":"https://pith.science/pith/HSOTSG6PY72ZNKM4PNX3WWQPZF.json","view_paper":"https://pith.science/paper/HSOTSG6P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.04750&json=true","fetch_graph":"https://pith.science/api/pith-number/HSOTSG6PY72ZNKM4PNX3WWQPZF/graph.json","fetch_events":"https://pith.science/api/pith-number/HSOTSG6PY72ZNKM4PNX3WWQPZF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HSOTSG6PY72ZNKM4PNX3WWQPZF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HSOTSG6PY72ZNKM4PNX3WWQPZF/action/storage_attestation","attest_author":"https://pith.science/pith/HSOTSG6PY72ZNKM4PNX3WWQPZF/action/author_attestation","sign_citation":"https://pith.science/pith/HSOTSG6PY72ZNKM4PNX3WWQPZF/action/citation_signature","submit_replication":"https://pith.science/pith/HSOTSG6PY72ZNKM4PNX3WWQPZF/action/replication_record"}},"created_at":"2026-05-17T23:51:28.797933+00:00","updated_at":"2026-05-17T23:51:28.797933+00:00"}