Dynamic sub-ontology evolution for traditional Chinese medicine web ontology.
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.
Mao
Yuxin
Y
School of Computer Science, Zhejiang University, Hangzhou, Zhejiang 310027, China. maoyx@zju.edu.cn
Wu
Zhaohui
Z
Tian
Wenya
W
Jiang
Xiaohong
X
Cheung
William K
WK
D000818:Animals; D003628:Database Management Systems; D005103:Expert Systems; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D020407:Internet / Q000458:organization & administration*; D051188:Knowledge Bases; D008516:Medicine, Chinese Traditional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011381:Programming Languages; D012660:Semantics; D018511:Systems Integration; D014584:User-Computer Interface; D018875:Vocabulary, Controlled