Canonicalizing Knowledge Base Literals

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

Enhancing the Conciseness of Linked Data by Discovering Synonym Predicates

International audienceIn the meantime of the rapidly growing of Linked Data, the quality of these datasets is yet a challenge. A close examination of the quality of this data could be very critical, especially if important researches or professional decisions depend on it. Nowadays, several Linked Data quality metrics have been proposed which cover numerous dimensions of Linked Data quality such as completeness, consistency, conciseness and interlinking. In this paper, we propose an approach to enhance the conciseness of linked datasets by discovering synonym predicates. This approach is based, in addition to a statistical analysis, on a deep semantic analysis of data and on learning algorithms. We argue that studying the meaning of predicates can help to improve the accuracy of results. A set of experiments are conducted on real-world datasets to evaluate the approach