Open AccessDETECTION OF SEMANTIC RELATIONS BASED ON KNOWLEDGE GRAPH
Author(s) -
Tạ Duy Công Chiến
Publication year - 2022
Publication title -
khoa học và công nghệ
Language(s) - English
Resource type - Journals
ISSN - 2525-2267
DOI - 10.46242/jstiuh.v52i05.4110
Subject(s) - wordnet , computer science , information retrieval , natural language processing , conceptual graph , graph , synonym (taxonomy) , artificial intelligence , information extraction , semantic web , knowledge graph , semantic network , knowledge representation and reasoning , theoretical computer science , botany , biology , genus
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.