Name
Title | ||
|---|---|---|
A Hybrid Approach using Ontology Similarity and Fuzzy Logic for Semantic Question Answering. |
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. |
Year | Venue | DocType |
|---|---|---|
2017 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1709.09214 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Monika Rani | 1 | 1 | 1.71 |
| Maybin K. Muyeba | 2 | 47 | 7.61 |
| O. P. Vyas | 3 | 121 | 14.28 |