Semantic relatedness in semantic networks

Abstract. This paper presents a new semantic relatedness measure on semantic networks (SN) that uses both hierarchical and non-hierarchical relations. Our approach relies on two assumptions. Firstly, in a given SN, only a few numbers of paths can be considered as "semantically correct" and these paths obey to a given set of rules. Secondly, following a given edge in a path has a cost (which depends on its type, is-a, part-o f, etc.) and its position in the path. We propose an evaluation of our measure on WordNet with two different benchmarks, using the part-o f relation. We show that, in this context, our measure does better than the classical semantic measures.

• Type: Conference
• Year: 2008
• Where: ECAI
• Authors: Laurent Mazuel, Nicolas Sabouret
• Keywords: Artificial Intelligence, ECAI 2008, Non-hierarchical Relations, Semantic Networks, Semantic Relatedness Measure