TransKGQA: Enhanced Knowledge Graph Question Answering With Sentence Transformers

You Li Chong, Chin Poo Lee, Shahrin Zen Muhd-Yassin, Kian Ming Lim, Ahmad Kamsani Samingan

Research output: Journal PublicationArticlepeer-review


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/all-MiniLM-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.

Original languageEnglish
Pages (from-to)74872-74887
Number of pages16
JournalIEEE Access
Publication statusPublished - 2024
Externally publishedYes


  • knowledge graph
  • machine learning
  • natural language processing
  • Neo4j
  • Question answering
  • sentence transformer

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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