Triple Scoring Using Paragraph Vector - The Gailan Triple Scorer at WSDM Cup 2017
This work addresses a specific data ranking problem in information retrieval for competition participants, presenting an incremental application of existing methods.
The paper tackled the WSDM Cup 2017 Triple Scoring task by generating relevance scores based on textual similarity between subject and value descriptions using Paragraph Vector, showing promising results for ranking values associated with a subject.
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.