Triple Scoring Using a Hybrid Fact Validation Approach - The Catsear Triple Scorer at WSDM Cup 2017
This work addresses information relevance sorting in knowledge bases for users needing prioritized data, but it is incremental as it builds on existing methods for a specific competition.
The paper tackled the problem of scoring triples in knowledge bases for relevance ranking by combining answers from three sources using a linear regression classifier, achieving an Accuracy2 value of 79.58% and 4th place in the WSDM Cup 2017 challenge.
With the continuous increase of data daily published in knowledge bases across the Web, one of the main issues is regarding information relevance. In most knowledge bases, a triple (i.e., a statement composed by subject, predicate, and object) can be only true or false. However, triples can be assigned a score to have information sorted by relevance. In this work, we describe the participation of the Catsear team in the Triple Scoring Challenge at the WSDM Cup 2017. The Catsear approach scores triples by combining the answers coming from three different sources using a linear regression classifier. We show how our approach achieved an Accuracy2 value of 79.58% and the overall 4th place.