基于需求评估响应的循环迭代本体构建方法

针对与IEEE 1074-1995软件开发标准相比成熟度较高的METHONTOLOGY方法和七步法中未考虑到本体质量评估及其响应的问题，提出一种基于需求评估响应的循环迭代本体构建新方法。首先，借鉴软件开发V-模型和本体测试框架对所建本体进行需求分析，以定义出一套强调满足需求而非知识丰富度的本体测试文档；其次，细化核心架构、架构知识体系，并更新测试文档；最后，利用测试文档分别评估核心架构、架构知识体系、需求分析上的知识可满足性表达，并在知识表达不满足的情况下进行本体的局部或全局更新。与本体构建常用方法相比，所提方法能实现本体构建过程中的评估与迭代演进。此外，运用该方法建立的政务本体不仅为事项办理相关知识提供了知识表征框架，还为政务知识计算提供了新思路。基于所提方法开发的政务流程优化程序已成功应用在某省的政务大数据分析领域，这在一定程度上证实了方法的合理性与有效性。

Cyclic iterative ontology construction method based on demand assessment and response

Aiming at the problem that the METHONTOLOGY method and the seven-step method, which are more mature than the IEEE 1074-1995 software development standard, do not consider the ontology quality assessment and its response, a new cyclic iterative ontology construction method based on demand assessment and response was proposed. First, based on the software development V-model and ontology testing framework, demand analysis for the constructed ontology was conducted, so as to define a set of ontology test design documents that emphasize meeting the demands rather than knowledge richness. Second, core architecture and architecture knowledge system were refined, and the test documents were updated. Finally, the expressions of knowledge satisfiability on the core architecture, architecture knowledge system and demand analysis were respectively evaluated by using the test documents, and the ontology was updated locally or globally when the expressions of knowledge were not satisfied. Compared with the common methods of ontology construction, the proposed method can realize the evaluation and iterative evolution in the ontology construction process. Furthermore, the government ontology established by this method not only provides a knowledge representation framework for the relevant knowledge of item transaction, but also provides a new idea for the calculation of government knowledge. And the developed government affair process optimization program based on the proposed method has successfully applied in a provincial government affair big data analysis field, so as to confirm the rationality and effectiveness of the method to a certain extent.