Canonicalizing Knowledge Base Literals
This addresses quality issues in ontology-based knowledge bases like DBpedia, improving their usefulness and usability for applications relying on structured data.
The paper tackles the problem of canonicalizing string literals in knowledge bases by replacing them with typed entities, proposing a framework that combines reasoning and machine learning to predict relevant entities and types, and evaluates it against state-of-the-art baselines for semantic typing and entity matching.
Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching.