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Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017

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arxiv 1712.08353 v1 pith:KM2N7TAF submitted 2017-12-22 cs.IR

Vibhor Kanojia , Riku Togashi , Hideyuki Maeda (Yahoo! Japan) This is my paper · ORCID
classification cs.IR
keywords entityknowledgeembeddinggraphmodelnationalitiesprofessionsrelevance
verification ladder T0 review T1 audit T2 compute T3 formal
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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-level semantics.

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