In this paper we describe our solution to the WSDM Cup 2017 Triple Scoring task. Our approach generates a relevance score based on the textual description of the triple's subject and value (Object). It measures how similar (related) the text description of the subject is to the text description of its values. The generated similarity score can then be used to rank the multiple values associated with this subject. We utilize the Paragraph Vector algorithm to represent the unstructured text into fixed length vectors. The fixed length representation is then employed to calculate the similarity (relevance) score between the subject and its multiple values. Our experimental results have shown that the suggested approach is promising and suitable to solve this problem.
@article{arxiv.1712.08360,
title = {Triple Scoring Using Paragraph Vector - The Gailan Triple Scorer at WSDM Cup 2017},
author = {Esraa Ali and Annalina Caputo and Séamus Lawless},
journal= {arXiv preprint arXiv:1712.08360},
year = {2017}
}
Comments
Triple Scorer at WSDM Cup 2017, see arXiv:1712.08081