A Hybrid Approach using Ontology Similarity and Fuzzy Logic for Semantic Question Answering

TL;DR

This paper proposes a hybrid semantic question answering system combining ontology similarity and fuzzy logic to improve answer accuracy in information retrieval.

Contribution

It introduces a novel hybrid approach using fuzzy co-clustering and ontology similarity for more precise semantic question answering.

Findings

Improved accuracy over non-fuzzy semantic ontology methods

Effective use of fuzzy type-1 and type-2 scales for prioritization

Enhanced document retrieval through hybrid semantic and fuzzy techniques

Abstract

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.