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.
@article{arxiv.1712.08353,
title = {Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017},
author = {Vibhor Kanojia and Riku Togashi and Hideyuki Maeda},
journal= {arXiv preprint arXiv:1712.08353},
year = {2017}
}
Comments
Triple Scorer at WSDM Cup 2017, see arXiv:1712.08081