Jan Morbach

Ontology-Based Integration and Management of Distributed Design Data

During the design phase of a chemical plant, information is created by various software tools and stored in different documents and databases. These distributed design data are a potential source of valuable knowledge, which could be exploited by novel software applications. However, before further processing, the scattered information has to be merged and consolidated. For this task, semantic technologies are a promising alternative to conventional database technology. This contribution gives an outline of the transfer project T1, which aims at the development of an ontology-based software prototype for the integration and reconciliation of design data. Both ontology and software development will be performed in close cooperation with partners from the chemical and software industries to ensure their compliance with the requirements of industrial practice. The advantages of semantic technologies will be demonstrated by comparing the prototype against a conventional integration solution.

Ontology-Based information management in design processes

Abstract Engineering design processes are highly creative and knowledge-intensive tasks that involve extensive information exchange and communication among diverse developers. In such dynamic settings, traditional information management systems fail to provide adequate support due to their inflexible data structures and hard-wired usage procedures, as well as their restricted ability to integrate processes and product information. In this paper, we advocate the idea of Process Data Warehousing as a means to provide an information management and integration platform for such design processes. The key idea behind our approach is a flexible ontology-based schema with formally defined semantics that enables the capture and reuse of design knowledge, supported by advanced computer science methods.

Scenario-Based Analysis of Industrial Work Processes

In this section, the modeling procedure for design processes introduced in Subsect. 2.4.2 is discussed from a more application-oriented point of view. The Workflow Modeling System WOMS, which has been developed for the easy modeling of complex industrial design processes, is described. Many case studies have been performed during the elaboration and validation of the modeling methodology and the tool, several of them in different industrial settings. In this contribution, some case studies are described in more detail. Two of them address different types of design processes. A third case study, demonstrating the generalizability of our results, deals with the work processes during the operation of a chemical plant.

Integrated Application Domain Models for Chemical Engineering

A comprehensive summary of the application domain models presented in this chapter is given, and their integration into a common framework is discussed. Other existing application domain models of comparable scope are reviewed and compared to the models presented herein.

An Introduction to Application Domain Modeling

This section serves as an introduction to application domain modeling. Firstly, we will motivate the objectives of modeling. Next, we will suggest definitions for different types of application domain models. Finally, a brief survey of the modeling languages applied throughout this chapter will be given.

Model Dependencies, Fine-Grained Relations, and Integrator Tools

The models developed within subprojects A2 and B2 together form one of the vertical columns of the process/product model. The application domain models of A2 are refined to tool models of B2 such that integrator tools can be realized. The process of building integrators is rather well understood in general, as is the process of refining the application domain models of A2 to tool models of B2. Nevertheless, important parts are missing for a concise and layered process/product model.

Related Work on Ontologies for Engineering Applications

This chapter gives an overview on the information models and ontologies that are thematically related to OntoCAPE. It is structured into two major parts: Section 11.1 reviews the previous work in the research group of the authors; that is, the information models preceding OntoCAPE are described, and the progress made over time is discussed. In Sect. 11.2, the work of other research groups is analyzed and compared against OntoCAPE.

Design Principles of OntoCAPE

Concluding the description of OntoCAPE, we will subsequently present the major principles according to which the ontology has been designed. Basically, design principles are objective criteria for guiding and evaluating the design decisions made during ontology development (Gruber 1995). A number of design principles for information modeling in general, and ontology engineering in particular, have been suggested in the literature (e.g., Gruber 1995; Fox and Gruninger 1998; Arpirez et al. 1998; Chandrasekaran et al. 1999; Gomez-Perez et al. 2004; Rector et al. 2004; Smith 2006; and others). Compliance with these acknowledged principles is a credible indicator for the quality of an ontology.

A semantic information model for data integration across the chemical process design process

Abstract Information integration during the design process of chemical plants is a long-standing and not sufficiently solved problem in industrial practice to date. The major challenge identified is the capturing of the informations semantics. Within an ongoing research project an ontology-based approach for information integration in process engineering design projects is developed. This contribution sketches the semantic information model applied in the integration software, which is based on the formal ontology OntoCAPE.

Overview on OntoCAPE

Having established the scientific background of ontology engineering, we now present version 2.0 of the ontology OntoCAPE. Compliant with the terminology introduced in the previous chapter, OntoCAPE can be characterized as a formal, heavyweight ontology, which is represented in the OWL modeling language. It consists of several sub-ontologies, which perform different functions: According to the classification framework introduced in Sect. 2.6, the individual sub-ontologies serve the functions of a meta ontology (at the logical metalevel), a top-level ontology, a domain ontology, as well as some application ontologies.