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

Collaborative Knowledge Bases such as Freebase [1] and Wikidata [2] mention multiple professions and nationalities for a particular entity. The goal of the WSDM Cup 2017 [3] 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. 1. TASK INTRODUCTION Many entities usually have multiple professions or nationalities, and it is often desirable to rank the relevance of these individual triplets. The goal of the challenge was to compute a score in the range [0, 7] that measures the relevance of the statement expressed by the individual triplet compared to other triplets from the same relation. Participants were provided with a list of 385,426 entities along with five files, • profession.kb: all professions for a set of 343,329 entities • nationality.kb: all nationalities for a set of 301,590 entities • profession.train: relevance scores for 515 tuples (pertaining to 134 entities) from profession.kb • nationality.kb: relevance scores for 162 tuples (pertaining to 77 entities) from nationality.kb • nationality.kb: 33,159,353 sentences from Wikipedia with annotations of the 385,426 entities Apart from these, the participants were allowed to use any kind or amount of additional data (except for human/judgements). The output of this task was to generate relevance scores for all the triplets, 0 being the lowest relevance, and 7 being the highest.