Ontology supported patent search architecture with natural language analysis and fuzzy rules

Abstract

We have recently witnessed a rapid growth in scientific information retrieval research related to patents. Retrieving relevant information from and about patents is a non-trivial task and poses many technical challenges. In this paper we present a new approach to patent search that combines semantic knowledge and ontologies used to annotate patents processed with natural language processing tools. The architecture uses fuzzy logic rules to organize the annotated patents and achieve more precise retrieval. Our approach to combine proven techniques in a composite architecture showed improved results compared to pure textual based indexing and retrieval. We also showed that results ranked using semantic annotation are better than results based on simple keyword frequencies. © 2013 Springer-Verlag.