Ontology-supported document ranking for novelty search

Abstract

Within specific domains, users generally face the challenge to populate an ontology according to their needs. Especially in case of novelty detection and forecast, the user wants to integrate novel information contained in natural text documents into his/her own ontology in order to utilise the knowledge base in a further step. In this paper, a semantic document ranking approach is proposed which serves as a prerequisite for ontology population. By using the underlying ontology for both query generation and document ranking, query and ranking are structured and, therefore, promise to provide a better ranking in terms of relevance and novelty than without using semantics. © 2013 Springer-Verlag Berlin Heidelberg.