Semantic similarity measures play an important role in information
retrieval and information integration. Traditional approaches to
modeling semantic similarity compute the semantic distance between
definitions within a single ontology. This single ontology is either
a domain-independent ontology or the result of the integration of
existing ontologies. We present an approach to computing semantic
similarity that relaxes the requirement of a single ontology and
accounts for differences in the levels of explicitness and formalization
of the different ontology specifications. A similarity function determines
similar entity classes by using a matching process over synonym sets,
semantic neighborhoods, and distinguishing features that are classified
into parts, functions, and attributes. Experimental results with
different ontologies indicate that the model gives good results when
ontologies have complete and detailed representations of entity classes.
While the combination of word matching and semantic neighborhood
matching is adequate for detecting equivalent entity classes, feature
matching allows us to discriminate among similar, but not necessarily
equivalent entity classes.
%0 Journal Article
%1 rodriguez2003
%A Rodriguez, M.A.
%A Egenhofer, M.J.
%D 2003
%J IEEE Transactions on Knowledge and Data Engineering
%K engineering, information integration, interoperability, knowledge management, matching, measures ontology retrieval, semantic similarity
%N 2
%P 442--456
%R 10.1109/TKDE.2003.1185844
%T Determining Semantic Similarity among Entity Classes from Different
Ontologies
%V 15
%X Semantic similarity measures play an important role in information
retrieval and information integration. Traditional approaches to
modeling semantic similarity compute the semantic distance between
definitions within a single ontology. This single ontology is either
a domain-independent ontology or the result of the integration of
existing ontologies. We present an approach to computing semantic
similarity that relaxes the requirement of a single ontology and
accounts for differences in the levels of explicitness and formalization
of the different ontology specifications. A similarity function determines
similar entity classes by using a matching process over synonym sets,
semantic neighborhoods, and distinguishing features that are classified
into parts, functions, and attributes. Experimental results with
different ontologies indicate that the model gives good results when
ontologies have complete and detailed representations of entity classes.
While the combination of word matching and semantic neighborhood
matching is adequate for detecting equivalent entity classes, feature
matching allows us to discriminate among similar, but not necessarily
equivalent entity classes.
@article{rodriguez2003,
abstract = {Semantic similarity measures play an important role in information
retrieval and information integration. Traditional approaches to
modeling semantic similarity compute the semantic distance between
definitions within a single ontology. This single ontology is either
a domain-independent ontology or the result of the integration of
existing ontologies. We present an approach to computing semantic
similarity that relaxes the requirement of a single ontology and
accounts for differences in the levels of explicitness and formalization
of the different ontology specifications. A similarity function determines
similar entity classes by using a matching process over synonym sets,
semantic neighborhoods, and distinguishing features that are classified
into parts, functions, and attributes. Experimental results with
different ontologies indicate that the model gives good results when
ontologies have complete and detailed representations of entity classes.
While the combination of word matching and semantic neighborhood
matching is adequate for detecting equivalent entity classes, feature
matching allows us to discriminate among similar, but not necessarily
equivalent entity classes.},
added-at = {2007-05-04T05:48:10.000+0200},
author = {Rodriguez, M.A. and Egenhofer, M.J.},
biburl = {https://www.bibsonomy.org/bibtex/2dd66194d51b931b083d83bfb7bae8e32/p_ansell},
description = {Context-aware business processes},
doi = {10.1109/TKDE.2003.1185844},
interhash = {2c4ef027822c01a72f5ac08db17d0f4e},
intrahash = {dd66194d51b931b083d83bfb7bae8e32},
issn = {1041-4347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {engineering, information integration, interoperability, knowledge management, matching, measures ontology retrieval, semantic similarity},
number = 2,
owner = {peter},
pages = {442--456},
pdf = {HonoursResearch/Rodriguez2003-DeterminingSemanticSimilarityAmongEntityClassesFromDifferentOntologies.pdf},
timestamp = {2007-05-04T05:48:13.000+0200},
title = {Determining Semantic Similarity among Entity Classes from Different
Ontologies},
volume = 15,
year = 2003
}