Paper 73 (Research track)

Inferring new types on large datasets applying ontology class hierarchy classifiers: the DBpedia case

Author(s): Mariano Rico, Idafen Santana-Pérez, Pedro Pozo-Jiménez, Asunción Gómez-Pérez

Abstract: Adding resource types information to resources belonging to large open knowledge graphs becomes a challenging task, specially when considering those that are generated collaboratively, such as DBpedia, which usually contain errors and noise produced during the transformation process from different data sources. This problem has gained attention during last years, due to the importance of being able to properly classify resources in order to efficiently exploit the information provided by the dataset. In this work we explore how new classification models can be applied to solve this issue, relying on the information defined by the ontology class hierarchy. We have evaluated our approaches against DBpedia, and we have compared them to the most relevant contributions available nowadays. Our system, using a cascade of predictive models, is able to assign more than 1 million new types with higher precision and recall

Keywords: DBpedia; machine learning; dataset; semantic web; linked data

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