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

Source: [A Hybrid Approach using Ontology Similarity and Fuzzy Logic for Semantic Question Answering](<https://www.sota2.com/research/paper/68fa63bf1e209131cea00a99>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:49:10.785Z

Authors: Monika Rani, Maybin K. Muyeba, O. P. Vyas

Published: 2017-08-05T15:46:53.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1709.09214>)

[PDF](<https://arxiv.org/pdf/1709.09214.pdf>)