RelSifter: Scoring Triples from Type-like Relations - The Samphire Triple Scorer at WSDM Cup 2017

TL;DR

RelSifter is a supervised learning method that scores type-like relation triples using local knowledge graph information, achieving competitive accuracy in the WSDM Cup 2017 Triple Score challenge.

Contribution

The paper introduces a novel approach leveraging second-degree neighbors in knowledge graphs to predict relevance scores for type-like relation triples.

Findings

Achieved 73% accuracy for 'profession' triples.

Achieved 78% accuracy for 'nationality' triples.

Performance is comparable to state-of-the-art methods.

Abstract

We present RelSifter, a supervised learning approach to the problem of assigning relevance scores to triples expressing type-like relations such as 'profession' and 'nationality.' To provide additional contextual information about individuals and relations we supplement the data provided as part of the WSDM 2017 Triple Score contest with Wikidata and DBpedia, two large-scale knowledge graphs (KG). Our hypothesis is that any type relation, i.e., a specific profession like 'actor' or 'scientist,' can be described by the set of typical "activities" of people known to have that type relation. For example, actors are known to star in movies, and scientists are known for their academic affiliations. In a KG, this information is to be found on a properly defined subset of the second-degree neighbors of the type relation. This form of local information can be used as part of a learning algorithm to predict relevance scores for new, unseen triples. When scoring 'profession' and 'nationality' triples our experiments based on this approach result in an accuracy equal to 73% and 78%, respectively. These performance metrics are roughly equivalent or only slightly below the state of the art prior to the present contest. This suggests that our approach can be effective for evaluating facts, despite the skewness in the number of facts per individual mined from KGs.