• Miss. Vijayalaxmi C.Deshmukh
• Dr. Dinkar. S. Bhosale

This paper displays a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Early work on semantic similarity methods has 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 methods, 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. As instances are already extracted from the textual corpus and annotated by concepts in KGs, graph-based IC is proposed to compute IC based on the distributions of concepts over instances. Through experiments performed on public word similarity datasets, we note that the wpath semantic similarity method has produced a statistically meaningful improvement over other semantic similarity techniques. Moreover, in a real category classification evaluation, the wpath method has shown the best review concerning accuracy and F score.

semantic similarity, knowledge base, classification, wpath, Information Content, graph, wordnet

"Computing Semantic Similarity of Concepts in Knowledge Graphs using Graph-based Information Content", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.892-897, September-2018, Available :http://www.jetir.org/papers/JETIR1809781.pdf

"Computing Semantic Similarity of Concepts in Knowledge Graphs using Graph-based Information Content", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp892-897, September-2018, Available at : http://www.jetir.org/papers/JETIR1809781.pdf