Solving Ontology Meta-Matching Problem Through an Evolutionary Algorithm With Approximate Evaluation Indicators and Adaptive Selection Pressure

Ontology applies commonly to solve the problem of heterogeneity of data in the Semantic Web, but the heterogeneity problem between two ontologies seriously affects their communication. As an effective method, ontology matching can address the problem above, whose core technique is the similarity mea...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access Vol. 9; pp. 3046 - 3064
Main Authors: Lv, Qing, Jiang, Chengcai, Li, He
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
adaptive selection pressure
Alignment
approximate evaluation indicator
Convergence
Evaluation
evolution algorithm
Evolutionary algorithms
Genetic algorithms
Heterogeneity
Indicators
Knowledge representation
Linguistics
Matching
Ontologies
Ontology
Ontology meta-matching
Power engineering
Semantic web
Similarity
similarity measure
Similarity measures
Syntactics
Weight measurement
ISSN:2169-3536, 2169-3536
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Ontology applies commonly to solve the problem of heterogeneity of data in the Semantic Web, but the heterogeneity problem between two ontologies seriously affects their communication. As an effective method, ontology matching can address the problem above, whose core technique is the similarity measure. A single similarity measure calculates the similarity value about a feature between two concepts, but none of the similarity measures can ensure their effectiveness in all context due to the diverse heterogeneous features between two ontologies. Therefore, multiple similarity measures are usually aggregated to improve the result's confidence. The problem that how to determine the optimal aggregating weights for the different similarity measures to obtain a high-quality alignment is called the meta-matching problem of ontology, which is modeled as a nonlinear problem with many local optimal solutions. Evolutionary Algorithm (EA) can represent an efficient methodology to address the ontology meta-matching problem, but EA-based ontology matching techniques suffer from the premature convergence and the requirement of a reference alignment to evaluate the solutions. To overcome the defects mentioned above, in this work, an improved EA-based matching approach is proposed, where two approximate evaluation indicators, i.e. pseudo-recall and pseudo-precision, are presented to evaluate the solution's quality, and an adaptive selection pressure is utilized to overcome the premature convergence. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)'s benchmark, and the experimental results will prove the effectiveness of our proposed method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3047875