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Semantic business process integration based on ontology alignment

机译:基于本体对齐的语义业务流程集成

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摘要

Innovation and agility should be provided to businesses by efficient collaboration (i.e., communication and sharing) between them. However, semantic heterogeneity between business processes is a serious problem for automatically supporting cooperation processes (e.g., knowledge sharing and querying-based interactions) between businesses. In order to overcome this problem, we propose a novel framework based on aligning business ontologies for integrating heterogeneous business processes. We can consider two types of alignment processes; (i) manual alignment for building a whole business process ontology in a business process management (BPM) system and (ii) automated alignment between business processes of different BPM systems. Thereby, the optimal integration between two business processes has to be discovered to maximize the summation of a set of partial similarities between semantic components consisting of the business processes. In particular, the semantic component are extracted from semantic annotations of business processes. For evaluating the proposed system, we have conducted experimentations by using 22 business process management systems, which are organized as six business alliances. We have assumed that business processes in a same BPM system should be built with a common ontologies. The proposed alignment method has shown about 71.3% of precision (65.4% of recall). In addition, we found out that alignment results are dependent on some characteristics of ontologies (e.g., depth and number of classes).
机译:应通过企业之间的有效协作(即通信和共享)为企业提供创新和敏捷性。但是,业务流程之间的语义异质性对于自动支持业务之间的协作流程(例如,知识共享和基于查询的交互)是一个严重的问题。为了克服这个问题,我们提出了一种基于对齐业务本体以集成异构业务流程的新颖框架。我们可以考虑两种类型的对齐过程: (i)在业务流程管理(BPM)系统中构建整个业务流程本体的手动调整,以及(ii)不同BPM系统的业务流程之间的自动调整。因此,必须发现两个业务流程之间的最佳集成,以使由业务流程组成的语义组件之间的一组部分相似性的总和最大化。特别是,语义组件是从业务流程的语义注释中提取的。为了评估提议的系统,我们使用22个业务流程管理系统进行了实验,这些系统被组织为六个业务联盟。我们已经假定应该使用相同的本体构建同一BPM系统中的业务流程。提出的对齐方法显示出约71.3%的精度(召回率的65.4%)。此外,我们发现对齐结果取决于本体的某些特征(例如,深度和类数)。

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