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We present RelSifter, a supervised learning approach to the problem ofassigning relevance scores to triples expressing type-like relations such as'profession' and 'nationality.' To provide additional contextual informationabout individuals and relations we supplement the data provided as part of theWSDM 2017 Triple Score contest with Wikidata and DBpedia, two large-scaleknowledge graphs (KG). Our hypothesis is that any type relation, i.e., aspecific profession like 'actor' or 'scientist,' can be described by the set oftypical "activities" of people known to have that type relation. For example,actors are known to star in movies, and scientists are known for their academicaffiliations. In a KG, this information is to be found on a properly definedsubset of the second-degree neighbors of the type relation. This form of localinformation can be used as part of a learning algorithm to predict relevancescores for new, unseen triples. When scoring 'profession' and 'nationality'triples our experiments based on this approach result in an accuracy equal to73% and 78%, respectively. These performance metrics are roughly equivalent oronly slightly below the state of the art prior to the present contest. Thissuggests that our approach can be effective for evaluating facts, despite theskewness in the number of facts per individual mined from KGs.

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