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Article Information:
Studying of Semantic Similarity Methods in Ontology
Vahideh Reshadat and Mohammad-Reza Feizi-Derakhshi
Corresponding Author: Vahideh Reshadat
Submitted: February 28, 2012
Accepted: March 16, 2012
Published: June 15, 2012 |
Abstract:
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Humans are able to easily judge if a pair of concepts are related in some way. Understanding of how
humans are able to perform this task is not easy. Semantic similarity denotes computing the similarity between
concepts, having the same meaning or related information, which are not necessarily lexically similar. Semantic
similarity between concepts plays an important role in Semantic Web, knowledge sharing, Web mining,
semantic sense understanding and text summarization. This also is an important problem in Natural Language
Processing and Information Retrieval Researches. These techniques are becoming important components of
most of the Information Retrieval (IR), Information Extraction (IE) and other intelligent knowledge based
systems. Therefore it has received considerable attention in the literature. Ontology has a good hierarchical
structure of concepts. In the ontology, semantic information can be realized through the semantic relationship
of concepts. Ontology-based semantic similarity techniques can estimate the semantic similarity between two
hierarchically expressed concepts in a given ontology or taxonomy. Semantic similarity is usually computed
by mapping concepts to ontology and by examining their relationships in it. The most popular semantic
similarity methods are implemented and evaluated using WordNet and MeSH. Several algorithmic approaches
for computing semantic similarity have been proposed. This paper discusses the various approaches used for
identifying semantically similar concepts in ontology.
Key words: Conceptual similarity, ontology, semantic similarity, similarity method, taxonomy, wordnet,
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Cite this Reference:
Vahideh Reshadat and Mohammad-Reza Feizi-Derakhshi, . Studying of Semantic Similarity Methods in Ontology. Research Journal of Applied Sciences, Engineering and Technology, (12): 1815-1821.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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