Affiliations: [a] Ho Chi Minh City University of Foreign Languages-Information Technology (HUFLIT), Ho Chi Minh City, Vietnam
| [b] The Industrial University of Ho Chi Minh City (IUH), Ho Chi Minh City, Vietnam
| [c] Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam
| [d] Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
Correspondence:
[*]
Corresponding author. Chien D.C. Ta, Faculty of Information Technology of The Industrial University of Ho Chi Minh City, Ho Chi Minh City 70000, Vietnam. E-mail: taduycongchien@iuh.edu.vn.
Abstract: Semantic relations have been adopted in many research fields, including the semantic web, information retrieval, and Q&A systems. The aim of the semantic relations is to remove conceptual and terminological confusion. This is achieved by specifying a set of general concepts that characterize domains and their definitions and interrelationships. This research describes how to detect semantic relations, including synonyms, hyponyms, and hypernym s based on WordNet and entities of a knowledge graph (KG). This KG was built from two resources: ACM Digital Library and Wikipedia. We used natural language processing and the deep learning approach for processing data before generating the KG with an effective algorithm. We chose five of 245 categories in the ACM Digital Library to evaluate the proposed method. The generated results show that our system has excellent performance.
Keywords: Semantic relations, knowledge graph, information extraction
