{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:K3SLAVWXG3DT5BICDFSIHXEJV5","short_pith_number":"pith:K3SLAVWX","canonical_record":{"source":{"id":"1809.01341","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-09-05T06:07:31Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"aec86e0a3e608051ce3bc54b01bc7f14631564208a490a9caca492628c6cba67","abstract_canon_sha256":"50e3f6218a18d970fb307095dca659026b60dc2893f8f539a6a9ca9ed592247f"},"schema_version":"1.0"},"canonical_sha256":"56e4b056d736c73e8502196483dc89af50c08b58d6af9ec42106a6e63284dccd","source":{"kind":"arxiv","id":"1809.01341","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.01341","created_at":"2026-05-18T00:06:15Z"},{"alias_kind":"arxiv_version","alias_value":"1809.01341v2","created_at":"2026-05-18T00:06:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.01341","created_at":"2026-05-18T00:06:15Z"},{"alias_kind":"pith_short_12","alias_value":"K3SLAVWXG3DT","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"K3SLAVWXG3DT5BIC","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"K3SLAVWX","created_at":"2026-05-18T12:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:K3SLAVWXG3DT5BICDFSIHXEJV5","target":"record","payload":{"canonical_record":{"source":{"id":"1809.01341","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-09-05T06:07:31Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"aec86e0a3e608051ce3bc54b01bc7f14631564208a490a9caca492628c6cba67","abstract_canon_sha256":"50e3f6218a18d970fb307095dca659026b60dc2893f8f539a6a9ca9ed592247f"},"schema_version":"1.0"},"canonical_sha256":"56e4b056d736c73e8502196483dc89af50c08b58d6af9ec42106a6e63284dccd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:15.808909Z","signature_b64":"G9+Wbzq1P1hd+bJ5D2JTi7feXZHUE9l2NW4d66dzDztzTUj6e19sqysdEm+JnwTC1dijnQvM1SZoTkl+K3erBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56e4b056d736c73e8502196483dc89af50c08b58d6af9ec42106a6e63284dccd","last_reissued_at":"2026-05-18T00:06:15.808276Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:15.808276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.01341","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ox/otIMLbb4n8x7HAJGHYsQTVRY739yaKDoMIO+BCv4dvbjhCotvnaT8HWQdNNGURP6EMJqW6t7jKVGMPykFBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T08:20:11.990571Z"},"content_sha256":"71a7764ae3716bd564e94880fe48aa6e23d351d4e188f1773bc3989df93499dd","schema_version":"1.0","event_id":"sha256:71a7764ae3716bd564e94880fe48aa6e23d351d4e188f1773bc3989df93499dd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:K3SLAVWXG3DT5BICDFSIHXEJV5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Embedding Multimodal Relational Data for Knowledge Base Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.AI","authors_text":"Liyan Chen, Pouya Pezeshkpour, Sameer Singh","submitted_at":"2018-09-05T06:07:31Z","abstract_excerpt":"Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in knowledge bases, such as text, images, and numerical values. In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data. Furt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.01341","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"poS1LdhTJrI7BSiBA0YAXC39JRoB+ULu/KYGAdxp53BLhPjBAcMrIbCyuxJRK7lMBB7R8gRLkNSjXPkIOQZADQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T08:20:11.990916Z"},"content_sha256":"5be1e1155ce268cfa338d4225e69ff819c5a3ea682d5ce45e23d054ad59f7c65","schema_version":"1.0","event_id":"sha256:5be1e1155ce268cfa338d4225e69ff819c5a3ea682d5ce45e23d054ad59f7c65"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K3SLAVWXG3DT5BICDFSIHXEJV5/bundle.json","state_url":"https://pith.science/pith/K3SLAVWXG3DT5BICDFSIHXEJV5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K3SLAVWXG3DT5BICDFSIHXEJV5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-20T08:20:11Z","links":{"resolver":"https://pith.science/pith/K3SLAVWXG3DT5BICDFSIHXEJV5","bundle":"https://pith.science/pith/K3SLAVWXG3DT5BICDFSIHXEJV5/bundle.json","state":"https://pith.science/pith/K3SLAVWXG3DT5BICDFSIHXEJV5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K3SLAVWXG3DT5BICDFSIHXEJV5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:K3SLAVWXG3DT5BICDFSIHXEJV5","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":"50e3f6218a18d970fb307095dca659026b60dc2893f8f539a6a9ca9ed592247f","cross_cats_sorted":["cs.CL","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-09-05T06:07:31Z","title_canon_sha256":"aec86e0a3e608051ce3bc54b01bc7f14631564208a490a9caca492628c6cba67"},"schema_version":"1.0","source":{"id":"1809.01341","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.01341","created_at":"2026-05-18T00:06:15Z"},{"alias_kind":"arxiv_version","alias_value":"1809.01341v2","created_at":"2026-05-18T00:06:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.01341","created_at":"2026-05-18T00:06:15Z"},{"alias_kind":"pith_short_12","alias_value":"K3SLAVWXG3DT","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"K3SLAVWXG3DT5BIC","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"K3SLAVWX","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:5be1e1155ce268cfa338d4225e69ff819c5a3ea682d5ce45e23d054ad59f7c65","target":"graph","created_at":"2026-05-18T00:06:15Z","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"},"paper":{"abstract_excerpt":"Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in knowledge bases, such as text, images, and numerical values. In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data. Furt","authors_text":"Liyan Chen, Pouya Pezeshkpour, Sameer Singh","cross_cats":["cs.CL","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-09-05T06:07:31Z","title":"Embedding Multimodal Relational Data for Knowledge Base Completion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.01341","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":"sha256:71a7764ae3716bd564e94880fe48aa6e23d351d4e188f1773bc3989df93499dd","target":"record","created_at":"2026-05-18T00:06:15Z","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":"50e3f6218a18d970fb307095dca659026b60dc2893f8f539a6a9ca9ed592247f","cross_cats_sorted":["cs.CL","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-09-05T06:07:31Z","title_canon_sha256":"aec86e0a3e608051ce3bc54b01bc7f14631564208a490a9caca492628c6cba67"},"schema_version":"1.0","source":{"id":"1809.01341","kind":"arxiv","version":2}},"canonical_sha256":"56e4b056d736c73e8502196483dc89af50c08b58d6af9ec42106a6e63284dccd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"56e4b056d736c73e8502196483dc89af50c08b58d6af9ec42106a6e63284dccd","first_computed_at":"2026-05-18T00:06:15.808276Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:15.808276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G9+Wbzq1P1hd+bJ5D2JTi7feXZHUE9l2NW4d66dzDztzTUj6e19sqysdEm+JnwTC1dijnQvM1SZoTkl+K3erBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:15.808909Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.01341","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:71a7764ae3716bd564e94880fe48aa6e23d351d4e188f1773bc3989df93499dd","sha256:5be1e1155ce268cfa338d4225e69ff819c5a3ea682d5ce45e23d054ad59f7c65"],"state_sha256":"28382eea17ee62aaba4f3eed16535db8c536700b1aeaa75e0169df1e36d4e420"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aInKj21ppWlGHmwEK5Jd5XDP1hdFpSFbcrdSKRN3tThvs/7/vFPJa5a69o4iWXlDoSJNxwRcVdYiAO1PJuPrBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T08:20:11.992828Z","bundle_sha256":"f236018330170012724381993385fc086b8a5aa666f001013c38e8d0eef4327e"}}