Optimizing ontology alignment through hybrid population-based incremental learning algorithm

Author:

Xue XingsiORCID,Chen Junfeng

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

Scientific Research Startup Foundation of Fujian University of Technology

Fujian Province Outstanding Young Scientific Researcher Training Project

Fundamental Research Funds for the Central Universities

Scientific Research Development Foundation of Fujian University of Technology

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,General Computer Science

Reference32 articles.

1. Acampora G, Loia V, Vitiello A (2013) Enhancing ontology alignment through a memetic aggregation of similarity measures. Inf Sci 250:1–20

2. Achichi M, Cheatham M, Dragisic Z, Euzenat J, Faria D, Ferrara A, Flouris G, Fundulaki I, Harrow I, Ivanova V et al (2016) Results of the ontology alignment evaluation initiative 2016. In: OM: Ontology matching, pp 73–129. No commercial editor

3. Baluja S (1994) Population-based incremental learning. a method for integrating genetic search based function optimization and competitive learning. Technical report, Carnegie-Mellon Univ Pittsburgh Pa Dept. of Computer Science

4. Bock J, Hettenhausen J (2012) Discrete particle swarm optimisation for ontology alignment. Inf Sci 192:152–173

5. Bodenreider O (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 32(Suppl 1):D267–D270

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