一种从企业遗留智能系统学习OWL本体方法研究

An approach to learning OWL ontology from enterprise legacy intelligent systems

为规避基于文本的本体学习中尚未解决的中文分词歧义问题,实现企业遗留智能系统中拥有的大量领域知识的复用,提出一种从遗留智能系统学习OWL本体的方法.在分析关系数据库模式、元组集与OWL本体之间元素对应关系的基础上,详述了该方法的具体步骤.与现有相关方法相比,本方法针对的数据源蕴含更丰富的领域知识,更加适合实际的工程应用;通过一个简单、低时间复杂度的转换算法,而非中间模型或大量抽象规则,从关系数据库模式中自动获取相应的OWL本体部分;并按照一定的先后顺序将遗留系统中范例、规则知识项(元组集)移植为OWL本体中对应的个体.一个面向企业工装工时定额的遗留智能系统应用实例证实了该方法的有效性.

In order to get around ambiguities in Chinese word segmentation and reuse a lot of domain knowledge in enterprise legacy intelligent system, a novel approach to learning OWL ontology from legacy intelligent systems is proposed. On the basis of analyzing the element correspondence between relational database schema, tuple set and OWL ontology, the approach is specified in detail. Compared with existing methods, the approach is more appropriate for actual engineering application and its data source implies more domain knowledge. OWL ontology from legacy intelligent systems can be acquired automatically via a simple translation algorithm with low time-complexity instead of using a middle mode/or a lot of abstract learning rules, and numerous knowledge items about rules and cases （tuple set） can be reused as instances of OWL ontology according to certain priority. Validation of the approach is done by an application instance learning OWL ontology from a legacy intelligent system in the wide range of tooling man-hour rationing.