TY - JOUR
T1 - TransKGQA: Enhanced Knowledge Graph Question Answering With Sentence Transformers
AU - Li Chong, You
AU - Poo Lee, Chin
AU - Zen Muhd-Yassin, Shahrin
AU - Ming Lim, Kian
AU - Kamsani Samingan, Ahmad
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - knowledge graph
KW - machine learning
KW - natural language processing
KW - Neo4j
KW - Question answering
KW - sentence transformer
UR - http://www.scopus.com/inward/record.url?scp=85194860823&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3405583
DO - 10.1109/ACCESS.2024.3405583
M3 - Article
AN - SCOPUS:85194860823
SN - 2169-3536
VL - 12
SP - 74872
EP - 74887
JO - IEEE Access
JF - IEEE Access
ER -