{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:KM2N7TAFIJUWLMUTDDZE7XYIBV","short_pith_number":"pith:KM2N7TAF","canonical_record":{"source":{"id":"1712.08353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-12-22T09:05:32Z","cross_cats_sorted":[],"title_canon_sha256":"bd18dbffe9b2eb07617be19da0489196e581e413b141849242ee815665b933e5","abstract_canon_sha256":"c1c1afefd50bf72687370673dd5b04815bfe90730c7a66ae9c4e400d2d3f7772"},"schema_version":"1.0"},"canonical_sha256":"5334dfcc05426965b29318f24fdf080d7df75dbf3d9afa762c63eb882988d915","source":{"kind":"arxiv","id":"1712.08353","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.08353","created_at":"2026-05-18T00:27:10Z"},{"alias_kind":"arxiv_version","alias_value":"1712.08353v1","created_at":"2026-05-18T00:27:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.08353","created_at":"2026-05-18T00:27:10Z"},{"alias_kind":"pith_short_12","alias_value":"KM2N7TAFIJUW","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"KM2N7TAFIJUWLMUT","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"KM2N7TAF","created_at":"2026-05-18T12:31:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:KM2N7TAFIJUWLMUTDDZE7XYIBV","target":"record","payload":{"canonical_record":{"source":{"id":"1712.08353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-12-22T09:05:32Z","cross_cats_sorted":[],"title_canon_sha256":"bd18dbffe9b2eb07617be19da0489196e581e413b141849242ee815665b933e5","abstract_canon_sha256":"c1c1afefd50bf72687370673dd5b04815bfe90730c7a66ae9c4e400d2d3f7772"},"schema_version":"1.0"},"canonical_sha256":"5334dfcc05426965b29318f24fdf080d7df75dbf3d9afa762c63eb882988d915","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:10.402789Z","signature_b64":"dDGAS8E0cO+kIakRXy5YF9blwxKkUwaMG+K4C11MJm4Q7MdNHSmiN4189n93sOgktig3G8NLlRGhmwJiOsBNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5334dfcc05426965b29318f24fdf080d7df75dbf3d9afa762c63eb882988d915","last_reissued_at":"2026-05-18T00:27:10.402190Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:10.402190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.08353","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:27:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mq93HgVlfbmPPtl+hbhkjULoRt6GZiuiuFp8PRsF4RrYrjoIx0KrJXZR7gd5zEwi4ArdWdP26wrhqjSyW1qZDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T12:44:45.655922Z"},"content_sha256":"4d3723fffa65a7d632131c896bd7bdbf817036e41c60c454af41c4d9c36a9289","schema_version":"1.0","event_id":"sha256:4d3723fffa65a7d632131c896bd7bdbf817036e41c60c454af41c4d9c36a9289"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:KM2N7TAFIJUWLMUTDDZE7XYIBV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Hideyuki Maeda (Yahoo! Japan), Riku Togashi, Vibhor Kanojia","submitted_at":"2017-12-22T09:05:32Z","abstract_excerpt":"Collaborative Knowledge Bases such as Freebase and Wikidata mention multiple professions and nationalities for a particular entity. The goal of the WSDM Cup 2017 Triplet Scoring Challenge was to calculate relevance scores between an entity and its professions/nationalities. Such scores are a fundamental ingredient when ranking results in entity search. This paper proposes a novel approach to ensemble an advanced Knowledge Graph Embedding Model with a simple bag-of-words model. The former deals with hidden pragmatics and deep semantics whereas the latter handles text-based retrieval and low-lev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.08353","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:27:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i/GRDoSaZ6AUsvCY2W591DtoUf9M9Wwq/LTPuVdU30r4ZSRi1zhY6gBqZoo0hwvKM7UOrRRV01svwGRNJonNDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T12:44:45.656514Z"},"content_sha256":"d7e317207816b1afd78d78f8f64911abe363331588177d247eea2c3a051c2bf0","schema_version":"1.0","event_id":"sha256:d7e317207816b1afd78d78f8f64911abe363331588177d247eea2c3a051c2bf0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KM2N7TAFIJUWLMUTDDZE7XYIBV/bundle.json","state_url":"https://pith.science/pith/KM2N7TAFIJUWLMUTDDZE7XYIBV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KM2N7TAFIJUWLMUTDDZE7XYIBV/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":"2026-08-17T12:44:45Z","links":{"resolver":"https://pith.science/pith/KM2N7TAFIJUWLMUTDDZE7XYIBV","bundle":"https://pith.science/pith/KM2N7TAFIJUWLMUTDDZE7XYIBV/bundle.json","state":"https://pith.science/pith/KM2N7TAFIJUWLMUTDDZE7XYIBV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KM2N7TAFIJUWLMUTDDZE7XYIBV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:KM2N7TAFIJUWLMUTDDZE7XYIBV","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":"c1c1afefd50bf72687370673dd5b04815bfe90730c7a66ae9c4e400d2d3f7772","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-12-22T09:05:32Z","title_canon_sha256":"bd18dbffe9b2eb07617be19da0489196e581e413b141849242ee815665b933e5"},"schema_version":"1.0","source":{"id":"1712.08353","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.08353","created_at":"2026-05-18T00:27:10Z"},{"alias_kind":"arxiv_version","alias_value":"1712.08353v1","created_at":"2026-05-18T00:27:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.08353","created_at":"2026-05-18T00:27:10Z"},{"alias_kind":"pith_short_12","alias_value":"KM2N7TAFIJUW","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"KM2N7TAFIJUWLMUT","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"KM2N7TAF","created_at":"2026-05-18T12:31:24Z"}],"graph_snapshots":[{"event_id":"sha256:d7e317207816b1afd78d78f8f64911abe363331588177d247eea2c3a051c2bf0","target":"graph","created_at":"2026-05-18T00:27:10Z","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":"Collaborative Knowledge Bases such as Freebase and Wikidata mention multiple professions and nationalities for a particular entity. The goal of the WSDM Cup 2017 Triplet Scoring Challenge was to calculate relevance scores between an entity and its professions/nationalities. Such scores are a fundamental ingredient when ranking results in entity search. This paper proposes a novel approach to ensemble an advanced Knowledge Graph Embedding Model with a simple bag-of-words model. The former deals with hidden pragmatics and deep semantics whereas the latter handles text-based retrieval and low-lev","authors_text":"Hideyuki Maeda (Yahoo! Japan), Riku Togashi, Vibhor Kanojia","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-12-22T09:05:32Z","title":"Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.08353","kind":"arxiv","version":1},"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":"sha256:4d3723fffa65a7d632131c896bd7bdbf817036e41c60c454af41c4d9c36a9289","target":"record","created_at":"2026-05-18T00:27:10Z","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":"c1c1afefd50bf72687370673dd5b04815bfe90730c7a66ae9c4e400d2d3f7772","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-12-22T09:05:32Z","title_canon_sha256":"bd18dbffe9b2eb07617be19da0489196e581e413b141849242ee815665b933e5"},"schema_version":"1.0","source":{"id":"1712.08353","kind":"arxiv","version":1}},"canonical_sha256":"5334dfcc05426965b29318f24fdf080d7df75dbf3d9afa762c63eb882988d915","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5334dfcc05426965b29318f24fdf080d7df75dbf3d9afa762c63eb882988d915","first_computed_at":"2026-05-18T00:27:10.402190Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:27:10.402190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dDGAS8E0cO+kIakRXy5YF9blwxKkUwaMG+K4C11MJm4Q7MdNHSmiN4189n93sOgktig3G8NLlRGhmwJiOsBNAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:27:10.402789Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.08353","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d3723fffa65a7d632131c896bd7bdbf817036e41c60c454af41c4d9c36a9289","sha256:d7e317207816b1afd78d78f8f64911abe363331588177d247eea2c3a051c2bf0"],"state_sha256":"c139d6eecd1bee0b2d297add441f49fbce7e37243a92e1f929752d35a0852d0b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I4RK9dD15/X7WvNgg2b7g7wa5BnUEqol/aOaP2TiA7Z16QVtduw2Jv2Y5VVWpgY/eWoe6GbYKJf49B8kVF8mAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T12:44:45.659776Z","bundle_sha256":"260f9c00dfe8c40fa08c9b2cdb180a7e7aed91fc3995a25627dda00b375f394d"}}