Named Entity Recognition for Ontology Population using Background Knowledge from Wikipedia

Abstract

Named Entity Recognition (NER) deals with identifying and classifying atomic texts into pre-defined ontological classes. It is the enabling technique to many complex knowledge acquisition tasks. The recent flourish of Web resources has opened new opportunities and challenges for knowledge acquisition. In the domain of NER and its application in ontology population, considerable research work has been dedicated to exploiting background knowledge from Web resources to enhance the accuracy of the system. This chapter gives a review of existing literature in this domain with an emphasis on using background knowledge extracted from the Web resources. The authors discuss the benefits of using background knowledge and the inadequacies of existing work. They then propose a novel method that automatically creates domain-specific background knowledge by exploring the Wikipedia knowledge base in a domain- and language-independent way. The authors empirically show that the method can be adapted to ontology population, and generates high quality background knowledge that improves the accuracy of domain-specific NER.