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Abstract
Semantic web is a one the concept of web mining. The aim of the project is find a duplicate logo from a large number of logo. This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WorldNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g., path length and depth), or only on the Information Content (IC) of concepts. We propose a semantic similarity method, namely wpath, to combine these two approaches, using IC to weight the shortest path length between concepts. Conventional corpus-based IC is computed from the distributions of concepts over textual corpus, which is required to prepare a domain corpus containing annotated concepts and has high computational cost. IC based on the distributions of concepts over instances. Through experiments performed on well-known word similarity datasets, we show that the w-path semantic similarity method has produced a statistically significant improvement over other semantic similarity methods.