2012
DOI: 10.1002/int.20517
|View full text |Cite
|
Sign up to set email alerts
|

A hybrid evolutionary approach for solving the ontology alignment problem

Abstract: Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean differe… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
32
0

Year Published

2012
2012
2021
2021

Publication Types

Select...
3
3
2

Relationship

2
6

Authors

Journals

citations
Cited by 73 publications
(32 citation statements)
references
References 28 publications
0
32
0
Order By: Relevance
“…); a linguistic one determines a similarity value between two entities by taking into account semantic relations such as synonymy and hypernymy; structural ones compute a similarity value between two entities by considering their kinship (parents and children). In this work, we consider the following similarity measures extracted by [16]: Entity Name Distance Measure and Comment Distance Measure among lexical similarity measures, Hierarchy Distance Measure and Domain and Range Restrictions Distance Measure belonging to structural ones and the linguistic Word Net Synonymy Name Distance Measure. Since the application of a single measure is often not enough to produce an acceptable output alignment, the common technique is to combine different matchers to compute a single aggregated similarity value.…”
Section: The Ontology Meta-matching Problemmentioning
confidence: 99%
“…); a linguistic one determines a similarity value between two entities by taking into account semantic relations such as synonymy and hypernymy; structural ones compute a similarity value between two entities by considering their kinship (parents and children). In this work, we consider the following similarity measures extracted by [16]: Entity Name Distance Measure and Comment Distance Measure among lexical similarity measures, Hierarchy Distance Measure and Domain and Range Restrictions Distance Measure belonging to structural ones and the linguistic Word Net Synonymy Name Distance Measure. Since the application of a single measure is often not enough to produce an acceptable output alignment, the common technique is to combine different matchers to compute a single aggregated similarity value.…”
Section: The Ontology Meta-matching Problemmentioning
confidence: 99%
“…Therefore, hereafter, we describe the chromosome structure representing an alignment, the fitness function which allows the evaluation of each alignment, and the integrated local search process so as defined in [5].…”
Section: A Memetic Ontology Alignment Systemmentioning
confidence: 99%
“…In particular, the investigated different memetic approaches are based on the memetic ontology alignment system presented in [5]. This system aligns two ontologies 1 and 2 and produces in output an alignment as follows.…”
Section: A Memetic Ontology Alignment Systemmentioning
confidence: 99%
See 1 more Smart Citation
“…The remaining offspring are copies of solutions from the previous generation. 48 With the exception of offspring produced through elitism, all offspring produced through crossover or otherwise are subjected to mutation. Typically a value in the range 60-90% is used for the crossover rate.…”
Section: Selectionmentioning
confidence: 99%