Dynamic sub-ontology evolution for traditional Chinese medicine web ontology

  • Authors:
  • Yuxin Mao;Zhaohui Wu;Wenya Tian;Xiaohong Jiang;William K. Cheung

  • Affiliations:
  • School of Computer Science, Zhejiang University, Hangzhou, Zhejiang 310027, China;School of Computer Science, Zhejiang University, Hangzhou, Zhejiang 310027, China;Information Technology Department, Zhejiang Economic & Trade Polytechnic, Hangzhou, Zhejiang 310018, China;School of Computer Science, Zhejiang University, Hangzhou, Zhejiang 310027, China;Computer Science Department, Hong Kong Baptist University, Kowloon Tong, Hong Kong

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2008

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Abstract

As a form of important domain knowledge, large-scale ontologies play a critical role in building a large variety of knowledge-based systems. To overcome the problem of semantic heterogeneity and encode domain knowledge in reusable format, a large-scale and well-defined ontology is also required in the traditional Chinese medicine discipline. We argue that to meet the on-demand and scalability requirement ontology-based systems should go beyond the use of static ontology and be able to self-evolve and specialize for the domain knowledge they possess. In particular, we refer to the context-specific portions from large-scale ontologies like the traditional Chinese medicine ontology as sub-ontologies. Ontology-based systems are able to reuse sub-ontologies in local repository called ontology cache. In order to improve the overall performance of ontology cache, we propose to evolve sub-ontologies in ontology cache to optimize the knowledge structure of sub-ontologies. Moreover, we present the sub-ontology evolution approach based on a genetic algorithm for reusing large-scale ontologies. We evaluate the proposed evolution approach with the traditional Chinese medicine ontology and obtain promising results.