In this example from DBpedia, starring is an activity pertinent to the profession Actor.

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

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.

Publication
WSDM Cup 2017 Notebook Papers, February 10, Cambridge, UK
Date

This work competed for the WSDM 2017 cup in the ‘Triple Scoring’ task. For more information, please refer to the official website of the cup.

BibTeX

@InProceedings{Shiralkar2017,
  Title                    = {{RelSifter: Scoring Triples from Type-like Relations---The Samphire Triple Scorer at WSDM Cup 2017}},
  Author                   = {Prashant Shiralkar and Mihai Avram and {Giovanni Luca} Ciampaglia and Filippo Menczer and Alessandro Flammini},
  Booktitle                = {{WSDM Cup 2017 Notebook Papers, February 10, Cambridge, UK}},
  Year                     = {2017},
  Editor                   = {Martin Potthast and Stefan Heindorf and Hannah Bast},
  Month                    = feb,
  Publisher                = {CEUR-WS.org},

  Crossref                 = {wsdmcup:2017},
  ISSN                     = {1613-0073},
  Url                      = {http://www.wsdm-cup-2017.org/proceedings.html}
}