{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:NS7XNOCLRRI5SMKNU62QJ4SJPT","short_pith_number":"pith:NS7XNOCL","canonical_record":{"source":{"id":"1705.02426","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-06T01:40:28Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"cb5e61a518124dad42961aa6e4635854e84baec1cd3dd07f9e0a939463d6b4d0","abstract_canon_sha256":"751b249de7c4ac39d444a15e489560ca49894a2b2cb05c5fc52e6807a58cd298"},"schema_version":"1.0"},"canonical_sha256":"6cbf76b84b8c51d9314da7b504f2497cc4cf8290f05337dd06c38599e8ab54ff","source":{"kind":"arxiv","id":"1705.02426","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.02426","created_at":"2026-05-18T00:40:48Z"},{"alias_kind":"arxiv_version","alias_value":"1705.02426v2","created_at":"2026-05-18T00:40:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.02426","created_at":"2026-05-18T00:40:48Z"},{"alias_kind":"pith_short_12","alias_value":"NS7XNOCLRRI5","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"NS7XNOCLRRI5SMKN","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"NS7XNOCL","created_at":"2026-05-18T12:31:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:NS7XNOCLRRI5SMKNU62QJ4SJPT","target":"record","payload":{"canonical_record":{"source":{"id":"1705.02426","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-06T01:40:28Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"cb5e61a518124dad42961aa6e4635854e84baec1cd3dd07f9e0a939463d6b4d0","abstract_canon_sha256":"751b249de7c4ac39d444a15e489560ca49894a2b2cb05c5fc52e6807a58cd298"},"schema_version":"1.0"},"canonical_sha256":"6cbf76b84b8c51d9314da7b504f2497cc4cf8290f05337dd06c38599e8ab54ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:48.658510Z","signature_b64":"PwcuKANS5O1QzOMfJHNe5tdC7R8XoCfMVZy9qBYithFLfiyBm4v6giJ1jintRIgUG73FUwPbF8+cdDTFlvXADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6cbf76b84b8c51d9314da7b504f2497cc4cf8290f05337dd06c38599e8ab54ff","last_reissued_at":"2026-05-18T00:40:48.657790Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:48.657790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.02426","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":"2026-05-18T00:40:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MIdNrBzehuBfh8mEq9Aen0/5wg5uAkgNXamfp1weear500fAr2KThuJj8ICcfrBXp2CIIbA01hvfpCYwkq7wAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T11:49:26.400160Z"},"content_sha256":"01bdcb8ffd34f5c936c933b0b3fd0a67f1f4cbbda11a0703e1348dafe08ae0f3","schema_version":"1.0","event_id":"sha256:01bdcb8ffd34f5c936c933b0b3fd0a67f1f4cbbda11a0703e1348dafe08ae0f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:NS7XNOCLRRI5SMKNU62QJ4SJPT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Analogical Inference for Multi-Relational Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Hanxiao Liu, Yiming Yang, Yuexin Wu","submitted_at":"2017-05-06T01:40:28Z","abstract_excerpt":"Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the \\textit{analogical} properties of the embedded entities and relations. By formulating the learning objective in a differentiable fashion, our model enjoys both theoretical power and computational scalab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.02426","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":"2026-05-18T00:40:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DA2wlFJXVHVhMTPGg5h2xQsJNqhtW79pByHLkVCiNof1wwffQbtAXd8gIgKlT6yhGj+GjsI+cBfbibKt5HmeAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T11:49:26.400518Z"},"content_sha256":"4d116ae3f18a627ff5cf8d66e9210bb56786d68053c989a3709b1fb72786fb95","schema_version":"1.0","event_id":"sha256:4d116ae3f18a627ff5cf8d66e9210bb56786d68053c989a3709b1fb72786fb95"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NS7XNOCLRRI5SMKNU62QJ4SJPT/bundle.json","state_url":"https://pith.science/pith/NS7XNOCLRRI5SMKNU62QJ4SJPT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NS7XNOCLRRI5SMKNU62QJ4SJPT/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":"2026-06-11T11:49:26Z","links":{"resolver":"https://pith.science/pith/NS7XNOCLRRI5SMKNU62QJ4SJPT","bundle":"https://pith.science/pith/NS7XNOCLRRI5SMKNU62QJ4SJPT/bundle.json","state":"https://pith.science/pith/NS7XNOCLRRI5SMKNU62QJ4SJPT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NS7XNOCLRRI5SMKNU62QJ4SJPT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:NS7XNOCLRRI5SMKNU62QJ4SJPT","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":"751b249de7c4ac39d444a15e489560ca49894a2b2cb05c5fc52e6807a58cd298","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-06T01:40:28Z","title_canon_sha256":"cb5e61a518124dad42961aa6e4635854e84baec1cd3dd07f9e0a939463d6b4d0"},"schema_version":"1.0","source":{"id":"1705.02426","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.02426","created_at":"2026-05-18T00:40:48Z"},{"alias_kind":"arxiv_version","alias_value":"1705.02426v2","created_at":"2026-05-18T00:40:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.02426","created_at":"2026-05-18T00:40:48Z"},{"alias_kind":"pith_short_12","alias_value":"NS7XNOCLRRI5","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"NS7XNOCLRRI5SMKN","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"NS7XNOCL","created_at":"2026-05-18T12:31:34Z"}],"graph_snapshots":[{"event_id":"sha256:4d116ae3f18a627ff5cf8d66e9210bb56786d68053c989a3709b1fb72786fb95","target":"graph","created_at":"2026-05-18T00:40:48Z","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":"Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the \\textit{analogical} properties of the embedded entities and relations. By formulating the learning objective in a differentiable fashion, our model enjoys both theoretical power and computational scalab","authors_text":"Hanxiao Liu, Yiming Yang, Yuexin Wu","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-06T01:40:28Z","title":"Analogical Inference for Multi-Relational Embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.02426","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":"sha256:01bdcb8ffd34f5c936c933b0b3fd0a67f1f4cbbda11a0703e1348dafe08ae0f3","target":"record","created_at":"2026-05-18T00:40:48Z","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":"751b249de7c4ac39d444a15e489560ca49894a2b2cb05c5fc52e6807a58cd298","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-06T01:40:28Z","title_canon_sha256":"cb5e61a518124dad42961aa6e4635854e84baec1cd3dd07f9e0a939463d6b4d0"},"schema_version":"1.0","source":{"id":"1705.02426","kind":"arxiv","version":2}},"canonical_sha256":"6cbf76b84b8c51d9314da7b504f2497cc4cf8290f05337dd06c38599e8ab54ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6cbf76b84b8c51d9314da7b504f2497cc4cf8290f05337dd06c38599e8ab54ff","first_computed_at":"2026-05-18T00:40:48.657790Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:40:48.657790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PwcuKANS5O1QzOMfJHNe5tdC7R8XoCfMVZy9qBYithFLfiyBm4v6giJ1jintRIgUG73FUwPbF8+cdDTFlvXADQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:40:48.658510Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.02426","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:01bdcb8ffd34f5c936c933b0b3fd0a67f1f4cbbda11a0703e1348dafe08ae0f3","sha256:4d116ae3f18a627ff5cf8d66e9210bb56786d68053c989a3709b1fb72786fb95"],"state_sha256":"448d08d988c2d7dc97890380b9f214a34f57539125dd2d05601782b648a06e9c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GkAfDXrIWDFO89aYRW0UlkdjdreHONsK43W6GYEhk4gfGZC62Dfuq0/3LasKMX64w0CBmvCW6cR5WR7FTArsDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T11:49:26.402419Z","bundle_sha256":"566bea5660b6a951d5acc97635749d25431c7e4cab7e4e40cde5a55fc6671112"}}