  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.
