Formalization of semantic annotation for systems interoperability in a PLM environment

International audienceNowadays, the need for systems collaboration across enterprises and through different domains has become more and more ubiquitous. Due to the lack of standardized models or architecture, as well as semantic mismatching and inconsistencies, research works on information and model exchange, trans-formation, discovery and reuse are carried out in recent years. One of the main challenges in these researches is to overcome the semantic gap between enterprise applications along any product lifecycle, involving many distributed and heterogeneous enterprise applications. We propose, in this paper, an approach for semantically annotating different knowledge views (business process models, business rules, conceptual models, and etc.) in the Product Lifecycle Management (PLM) environment. These formal semantic annotations will make explicit the tacit knowledge generally engraved in application models and act as bridges to support all actors in along the product lifecycle. A case study based on a specific manufacturing process will be presented for demonstrating how our semantic annotations can be applied in a Business to Manufacturing (B2M) interoperability contex

Semantic Model Alignment for Business Process Integration

Business process models describe an enterprise’s way of conducting business and in this form the basis for shaping the organization and engineering the appropriate supporting or even enabling IT. Thereby, a major task in working with models is their analysis and comparison for the purpose of aligning them. As models can differ semantically not only concerning the modeling languages used, but even more so in the way in which the natural language for labeling the model elements has been applied, the correct identification of the intended meaning of a legacy model is a non-trivial task that thus far has only been solved by humans. In particular at the time of reorganizations, the set-up of B2B-collaborations or mergers and acquisitions the semantic analysis of models of different origin that need to be consolidated is a manual effort that is not only tedious and error-prone but also time consuming and costly and often even repetitive. For facilitating automation of this task by means of IT, in this thesis the new method of Semantic Model Alignment is presented. Its application enables to extract and formalize the semantics of models for relating them based on the modeling language used and determining similarities based on the natural language used in model element labels. The resulting alignment supports model-based semantic business process integration. The research conducted is based on a design-science oriented approach and the method developed has been created together with all its enabling artifacts. These results have been published as the research progressed and are presented here in this thesis based on a selection of peer reviewed publications comprehensively describing the various aspects