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Submitted URL: http://dx.doi.org/10.1109/BIBM55620.2022.9995356
Effective URL: https://ieeexplore.ieee.org/document/9995356/
Submission: On January 05 via manual from AT — Scanned from DE
Effective URL: https://ieeexplore.ieee.org/document/9995356/
Submission: On January 05 via manual from AT — Scanned from DE
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Conferences >2022 IEEE International Confe...
EFFICIENT BIOMEDICAL ONTOLOGY META-MATCHING BASED ON INTERPOLATION MODEL BASED
HYBRID EVOLUTIONARY ALGORITHM
Publisher: IEEE
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Xingsi Xue; Qi Wu; Miao Ye; Hai Zhu; Yikun Huang
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Abstract
Document Sections
* I.
Introduction
* II.
Preliminaries
* III.
Evolutionary Algorithm with Interpolation Model
* IV.
Experiment
* V.
Conclusion and Future Work
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Abstract:As an advanced biomedical knowledge modeling technology, biomedical
ontology models the biomedical domain. However, since the lack of uniform
standards for constructing b...View more
Metadata
Abstract:
As an advanced biomedical knowledge modeling technology, biomedical ontology
models the biomedical domain. However, since the lack of uniform standards for
constructing biomedical ontologies, the biomedical ontologies obtained by
different construction methods for the same thing may be different, which is
known as the biomedical ontology heterogeneity problem. To solve this problem,
we need to execute the biomedical ontology matching process, where it is
important to integrate different similarity measures to improve the quality of
alignment. Evolutionary Algorithm (EA) is an effective algorithm to address the
biomedical ontology meta-matching problem. However, the classical EA-based
biomedical ontology meta-matching technique needs to traverse reference
alignment to evaluate the individuals, which makes the algorithm have high
running time. To overcome this drawback, we propose an Interpolation Model (IM)
based Hybrid EA (IM-HEA), which combines EA with a problem-specific IM to
evaluate the individual and execute the local search process. In particular, we
use lattice design to divide the feasible domain into several uniform
sub-regions, and on this basis, approximately evaluate the fitness of newly
generated individual. In addition, to avoid the algorithm falling into the local
optimum, we further introduce an IM-based local search process into EA’s
evolving process. In the experiment, we test IM-HEA on OAEI’s Benchmark and
Anatomy and compared them with classical EA in terms of alignment’s quality and
running time. The experimental results show that IM-HEA greatly enhances the
efficiency of EA with little sacrifice on the alignment’s quality.
Published in: 2022 IEEE International Conference on Bioinformatics and
Biomedicine (BIBM)
Date of Conference: 06-08 December 2022
Date Added to IEEE Xplore: 02 January 2023
ISBN Information:
DOI: 10.1109/BIBM55620.2022.9995356
Publisher: IEEE
Conference Location: Las Vegas, NV, USA
Funding Agency:
Contents
--------------------------------------------------------------------------------
I. INTRODUCTION
Semantic Web (SW) is an intelligent network capable of understanding words and
concepts and the logical relationships between them, which is proposed to define
and relate data in a computer-understood way [1], so it is particularly
important how to define the data, and ontologies meet this need. As a normative
modeling tool, an ontology is defined as a formal, explicit specification of a
shared conceptualization [2]. With the continuous advancement of ontology
technology, it has been widely used in various fields, including the biomedical
field. In the development of the biomedical field, researchers have constructed
various biomedical ontologies to facilitate their work [3]. Fig. 1 shows a
biomedical ontology. However, since different researchers have different
understandings of biomedical ontologies, this makes even the biomedical
ontologies created for the same thing different, which is called the biomedical
ontology heterogeneity problem [4]. The biomedical ontology heterogeneity
problem is a serious obstacle to the purpose of knowledge sharing. Biomedical
ontology matching is an effective way to address the biomedical ontology
heterogeneity problem through finding the connections between entities of
different biomedical ontologies [5], i.e., the mapping between biomedical
ontologies to bridge their semantic gaps.
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