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Matching Biomedical Ontologies with Compact Evolutionary Algorithm
- Source :
- Lecture Notes in Computer Science ISBN: 9783030604691
- Publication Year :
- 2020
- Publisher :
- Springer International Publishing, 2020.
-
Abstract
- Literature Based Discovery (LBD) aims at building bridges between existing literatures and discovering new knowledge from them. Biomedical ontology is such a literature that provides an explicit specification on biomedical knowledge, i.e., the formal specification of the biomedical concepts and data, and the relationships between them. However, since biomedical ontologies are developed and maintained by different communities, the same biomedical information or knowledge could be defined with different terminologies or in different context, which makes the integration of them becomes a challenging problem. Biomedical ontology matching can determine the semantically identical biomedical concepts in different biomedical ontologies, which is regarded as an effective methodology to bridge the semantic gap between two biomedical ontologies. Currently, Evolutionary Algorithm (EA) is emerging as a good methodology for optimizing the ontology alignment. However, EA requires huge memory consumption and long runtime, which make EA-based matcher unable to efficiently match biomedical ontologies. To overcome these problems, in this paper, we define a discrete optimal model for biomedical ontology matching problem, and utilize a compact version of Evolutionary Algorithm (CEA) to solve it. In particular, CEA makes use of a Probability Vector (PV) to represent the population to save the memory consumption, and introduces a local search strategy to improve the algorithm’s search performance. The experiment exploits Anatomy track, Large Biomed track and Disease and Phenotype track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CEA-based approach can effectively reduce the runtime and memory consumption of EA-based matcher, and determine high-quality biomedical ontology alignments.
- Subjects :
- education.field_of_study
Computer science
business.industry
Population
Evolutionary algorithm
Ontology (information science)
Machine learning
computer.software_genre
Open Biomedical Ontologies
Literature-based discovery
Formal specification
Artificial intelligence
education
business
Ontology alignment
computer
Semantic gap
Subjects
Details
- ISBN :
- 978-3-030-60469-1
- ISBNs :
- 9783030604691
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783030604691
- Accession number :
- edsair.doi...........ef9037d48a1e45df6c4f99f6e10ec4b0