DETECTION OF SEMANTIC RELATIONS BASED ON KNOWLEDGE GRAPH

Abstract

Semantic relations have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. Purpose of semantic relations is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper describes how to detect semantic relations, including synonym, hyponym and hypernym relations based on WordNet and entities of Knowledge Graph. This Knowledge graph is built from two main resources: Wikipedia and unstructured files from ACM Digital Library. We used Natural Language Processing (NLP) and Deep Learning for processing data before putting into Knowledge Graph. We choose 5 of 245 categories in the ACM Digital Library to evaluate our results. Results generated show that our system yields superior performance.