The WSDM Cup 2017 Triple scoring challenge is aimed at calculating and assigning relevance scores for triples from type-like relations. Such scores are a fundamental ingredient for ranking results in entity search. In this paper, we propose a method that uses neural embedding techniques to accurately calculate an entity score for a triple based on its nearest neighbor. We strive to develop a new latent semantic model with a deep structure that captures the semantic and syntactic relations between words. Our method has been ranked among the top performers with accuracy - 0.74, average score difference - 1.74, and average Kendall's Tau - 0.35.
@article{arxiv.1712.08359,
title = {Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding - The Cabbage Triple Scorer at WSDM Cup 2017},
author = {Yael Brumer and Bracha Shapira and Lior Rokach and Oren Barkan},
journal= {arXiv preprint arXiv:1712.08359},
year = {2017}
}
Comments
Triple Scorer at WSDM Cup 2017, see arXiv:1712.08081