Citation

Chong, You Li and Lee, Chin Poo and Muhd Yassin, Shahrin Zen and Lim, Kian Ming and Samingan, Ahmad Kamsani (2024) TransKGQA: Enhanced Knowledge Graph Question Answering With Sentence Transformers. IEEE Access, 12. pp. 74872-74887. ISSN 2169-3536

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Abstract

Knowledge Graph Question Answering (KGQA) plays a crucial role in extracting valuable insights from interconnected information. Existing methods, while commendable, face challenges such as contextual ambiguity and limited adaptability to diverse knowledge domains. This paper introduces TransKGQA, a novel approach addressing these challenges. Leveraging Sentence Transformers, TransKGQA enhances contextual understanding, making it adaptable to various knowledge domains. The model employs question-answer pair augmentation for robustness and introduces a threshold mechanism for reliable answer retrieval. TransKGQA overcomes limitations in existing works by offering a versatile solution for diverse question types. Experimental results, notably with the sentence-transformers/allMiniLM-L12-v2 model, showcase remarkable performance with an F1 score of 78%. This work advances KGQA systems, contributing to knowledge graph construction, enhanced question answering, and automated Cypher query execution.

Item Type:	Article
Uncontrolled Keywords:	machine learning, natural language processing
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jul 2024 02:56
Last Modified:	03 Jul 2024 02:56
URII:	http://shdl.mmu.edu.my/id/eprint/12590

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