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arXiv · 1712.08359

Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding - The Cabbage Triple Scorer at WSDM Cup 2017

Yael Brumer·Bracha Shapira·Lior Rokach·Oren Barkan
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Abstract

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.

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Yael Brumer, Bracha Shapira, Lior Rokach, Oren Barkan. 2017-12-22. Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding - The Cabbage Triple Scorer at WSDM Cup 2017. https://arxiv.org/abs/1712.08359

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