为解决复杂产品领域本体规模庞大导致本体应用效率低的问题, 提出一种支持复杂产品工程的领域子本体自动提取方法. 该方法以用户标识的感兴趣节点为起始节点, 采用节点相关度的概念替代本体节点之间的结构相似度来构建相关度矩阵, 通过相关度迭代扩张更新相关度矩阵, 提取相关度较高的节点, 还原节点之间的关系后获得一种外扩型的领域子本体; 在相关度迭代扩张方面, 分别从本体的结构关系和约束关系2个维度进行启发式搜索扩张, 并通过扩张因子实现相关度的逐级衰减; 在相关度矩阵的更新方面, 提出最大扩张和调和扩张2种机制; 最后以机器人某领域本体为例对领域子本体的复杂度进行评价, 同时给出了不同参数条件下文中方法提取领域子本体的规模和复杂度.%To solve the problem that the scale of complex product domain ontology is too large to be applied efficiently, a method of domain sub-ontology extraction for complex product knowledge management was proposed. Initial entities in domain ontology were identified by end-users. Relevancy between entities was employed to replace the similarity. Relevancy matrix was built and updated by expanding the relevancies it-eratively. Expansion type domain sub-ontology could be acquired by restoring the relations of entities. Enti-ties were expanded with heuristic search at the dimensions of structured relations and restrictive relations in ontology, and the relevancies between entities were attenuated by expansion factors. Maximum expansion-ism and harmonic expansionism were presented to update relevancy matrix. Finally, robotics domain was taken as the example. Domain ontology was built to evaluate the complexities of domain sub-ontologies, the scales and complexities of domain sub-ontologies were showed at different parameter conditions.
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