Abstract
With tremendous progress attained towards AI, understanding context from user queries has become a focus area to provide precise answers. In this direction, recent machine learning-based approaches are trying to extract semantic features from queries. Being keywords driven, these approaches underperform to cater semantics in the queries. To overcome this reliance on syntactic representations, semantics-based methods utilizing Knowledge Bases are being augmented to the query processing systems. In this paper, a novel semantic search framework over Knowledge Bases has been proposed and implemented. Semantic similarity between query and predicates is computed using BERTbased embeddings. Additionally, a dataset of semantically similar sentence variations is generated using ChatGPT for analysing the accuracy of the implemented system over YAGO Knowledge Base.
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