| Authors: |
M.A. Rodriguez
and M.J. Egenhofer
|
| Description: |
Context-aware business processes |
| Tags: |
engineering,
information
integration,
interoperability,
knowledge
management,
matching,
measures
ontology
retrieval,
semantic
similarity
|
| 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. |
@article{rodriguez2003,
title = {Determining Semantic Similarity among Entity Classes from Different
Ontologies},
author = {M.A. Rodriguez and M.J. Egenhofer},
journal = {IEEE Transactions on Knowledge and Data Engineering},
number = {2},
pages = {442--456},
volume = {15},
year = {2003},
description = {Context-aware business processes},
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.},
issn = {1041-4347}, doi = {10.1109/TKDE.2003.1185844}, owner = {peter}, pdf = {HonoursResearch/Rodriguez2003-DeterminingSemanticSimilarityAmongEntityClassesFromDifferentOntologies.pdf}, timestamp = {2006.03.31 12:25},
keywords = {engineering, information integration, interoperability, knowledge management, matching, measures ontology retrieval, semantic similarity }
}
::