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

Research output: Working paper/PreprintPreprint

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.
Original languageEnglish
StatePublished - 2017

Fingerprint

Dive into the research topics of 'Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding-The Cabbage Triple Scorer at WSDM Cup 2017'. Together they form a unique fingerprint.

Cite this