TransKGQA: Enhanced Knowledge Graph Question Answering With Sentence Transformers

TransKGQA: Enhanced Knowledge Graph Question Answering With Sentence Transformers

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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, Accessories - Bags - Backpacks 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 Bike Accessories - Lights 78%.This work advances KGQA systems, contributing to knowledge graph construction, enhanced question answering, and automated Cypher query execution.