• Vijayalaxmi C. Deshmukh
• Jagannath Nalavade

This paper shows how to assess the semantic similarity of concepts using Knowledge Graphs (KGs) such as WordNet and DBpedia. The topology of the semantic network linking ideas (e.g., path length and depth) or simply the Information Content (IC) of concepts have been the subject of previous semantic similarity research. By using IC to weight the shortest path length between ideas, we present a semantic similarity strategy that combines these two techniques. Traditional corpus-based IC, which is required to construct a domain corpus with annotated concepts and has a significant computing cost, estimates the distributions of ideas throughout a textual corpus. Graph-based IC is offered as a strategy for computing IC using concept distributions across instances since examples are already retrieved from textual corpora and labelled with ideas in KGs. By a statistically substantial margin, the wpath semantic similarity strategy beat the prior method. Previous semantic similarity approaches, which were based onUsing well-known word similarity datasets as a test Furthermore, in a real-world category categorization evaluation, the wpath technique had the highest accuracy and F score.

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

"Enhancing Semantic Similarity of Concepts in Knowledge Graphs ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 12, page no.c752-c759, December-2022, Available :http://www.jetir.org/papers/JETIR2211289.pdf

"Enhancing Semantic Similarity of Concepts in Knowledge Graphs ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 12, page no. ppc752-c759, December-2022, Available at : http://www.jetir.org/papers/JETIR2211289.pdf