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On the similarity relation within fuzzy ontology components
Ontology reuse is an important research issue. Ontology
merging, integration, mapping, alignment and versioning
are some of its subprocesses. A considerable research work has
been conducted on them. One common issue to these subprocesses
is the problem of defining similarity relations among ontologies
components. Crisp ontologies become less suitable in all domains
in which the concepts to be represented have vague, uncertain
and imprecise definitions. Fuzzy ontologies are developed to
cope with these aspects. They are equally concerned with the
problem of ontology reuse. Defining similarity relations within
fuzzy context may be realized basing on the linguistic similarity
among ontologies components or may be deduced from their
intentional definitions. The latter approach needs to be dealt
with differently in crisp and fuzzy ontologies. This is the scope
of this paper.ou
Implementation of an efficient Fuzzy Logic based Information Retrieval System
This paper exemplifies the implementation of an efficient Information
Retrieval (IR) System to compute the similarity between a dataset and a query
using Fuzzy Logic. TREC dataset has been used for the same purpose. The dataset
is parsed to generate keywords index which is used for the similarity
comparison with the user query. Each query is assigned a score value based on
its fuzzy similarity with the index keywords. The relevant documents are
retrieved based on the score value. The performance and accuracy of the
proposed fuzzy similarity model is compared with Cosine similarity model using
Precision-Recall curves. The results prove the dominance of Fuzzy Similarity
based IR system.Comment: arXiv admin note: substantial text overlap with
http://ntz-develop.blogspot.in/ ,
http://www.micsymposium.org/mics2012/submissions/mics2012_submission_8.pdf ,
http://www.slideshare.net/JeffreyStricklandPhD/predictive-modeling-and-analytics-selectchapters-41304405
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