by Euripides Petrakis, Giannis Varelas, Angelos Hliaoutakis, Paraskevi Raftopoulou
Abstract:
Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches to computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. The focus of this work is also on cross ontology methods which are capable of computing the semantic similarity between terms stemming from different ontologies (WordNet and MeSH in this work). This is a far more difficult problem (than the single ontology one referred to above) which has not been investigated adequately in the literature. X-Similarity, a novel cross-ontology similarity method is also a contribution of this work. All methods examined in this work are integrated into a semantic similarity system which is accessible on the Web. 1.
Reference:
X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies (Euripides Petrakis, Giannis Varelas, Angelos Hliaoutakis, Paraskevi Raftopoulou), In Journal of Digital Information Management, Journal of Digital Information Management (JDIM, volume 4, 2006.
Bibtex Entry:
@article{Petrakis2006,
abstract = {Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches to computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. The focus of this work is also on cross ontology methods which are capable of computing the semantic similarity between terms stemming from different ontologies (WordNet and MeSH in this work). This is a far more difficult problem (than the single ontology one referred to above) which has not been investigated adequately in the literature. X-Similarity, a novel cross-ontology similarity method is also a contribution of this work. All methods examined in this work are integrated into a semantic similarity system which is accessible on the Web. 1.},
annote = {
From Duplicate 1 (
Design and Evaluation of Semantic Similarity Measures for Concepts Stemming from the Same or Different Ontologies object instrumentality
- Petrakis, Euripides; Varelas, Giannis; Hliaoutakis, Angelos; Raftopoulou, Paraskevi )
And Duplicate 2 (
X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies
- Petrakis, Euripides; Varelas, Giannis; Hliaoutakis, Angelos; Raftopoulou, Paraskevi )
},
author = {Petrakis, Euripides and Varelas, Giannis and Hliaoutakis, Angelos and Raftopoulou, Paraskevi},
journal = {Journal of Digital Information Management},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
number = {4},
pages = {233},
publisher = {Journal of Digital Information Management (JDIM},
title = {{X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies}},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.3247},
volume = {4},
year = {2006}
}