A Hybrid Approach using Ontology Similarity and Fuzzy Logic for Semantic Question Answering
This work addresses the problem of enhancing information retrieval for users by offering more precise answers to queries with uncertainty, though it appears incremental as it builds on existing semantic and fuzzy logic techniques.
The paper tackled the challenge of improving answer accuracy in semantic question answering by developing a hybrid approach that combines ontology similarity with fuzzy logic, resulting in a retrieval system that provides more accurate answers than non-fuzzy semantic ontology methods.
One of the challenges in information retrieval is providing accurate answers to a user's question often expressed as uncertainty words. Most answers are based on a Syntactic approach rather than a Semantic analysis of the query. In this paper, our objective is to present a hybrid approach for a Semantic question answering retrieval system using Ontology Similarity and Fuzzy logic. We use a Fuzzy Co-clustering algorithm to retrieve the collection of documents based on Ontology Similarity. The Fuzzy Scale uses Fuzzy type-1 for documents and Fuzzy type-2 for words to prioritize answers. The objective of this work is to provide retrieval system with more accurate answers than non-fuzzy Semantic Ontology approach.