Legacy Information Systems (LIS) pose a challenge for many organizations. On one hand, LIS are viewed as aging systems needing replacement; on the other hand, years of accumulated business knowledge have made these systems mission-critical. Current approaches however are often criticized for being overtly dependent on technology and ignoring the business knowledge which resides within LIS. In this light, this paper proposes a means of capturing the business knowledge in a technology agnostic manner and transforming it in a way that reaps the benefits of clear semantic expression – this transformation is achieved via the careful use of ontology. The approach called Content Sophistication (CS) aims to provide a model of the business that more closely adheres to the semantics and relationships of objects existing in the real world. The approach is illustrated via an example taken from a case study concerning the renovation of a large financial system and the outcome of the approach results in technology agnostic models that show improvements along several dimensions.

The organizational environment increasingly demands that computer-based information systems are responsive to change and can work with each other seamlessly (ideally from a dynamic perspective). Given the large investment that organizations have in mission-critical legacy systems, evolutionary maintenance and systems integration now form a very significant part of the cost and effort profile of systems development. In terms of the integration issue, much of the difficulty lies in the fact that different systems often contain different ‘representations’ of the world. In the development process, it is generally accepted that the ‘information’ an information system contains about its business domain(s) is an essential intellectual part of the system, and the domain of fundamental concern. This concern is generally regarded as unitary, however, requiring no further breakdown into parts and it is commonly perceived that its relation to the business information system is simple and direct.

This paper examines the development of a conceptual model that defines Shell’s information requirements - the Downstream Data Model (DDM). The model has its roots in a framework based on the notion of ontological commitment and the focus of the analysis seeks to provide useful insights into the metaphysical aspects relevant to the creation and deployment of the DDM – primarily that related to the extensional nature of the model. The impact of this choice and the methodology employed in the production of the model is examined through example patterns covering spatial and temporal dissectiveness and the use of powerclasses. Having been through the experience of conceptual model development, the work concludes that the separation of the implementational and epistemological ‘gloss’ from a studied understanding of ontological commitment is a necessary evolution of practice in conceptual modelling.

An overview of 42 Objects' approach to Business Ontology Based Software Development that aims to secure a measure of agreement on:

• What philosophical ontology is,
• Whether, and how, ontology can assist in object oriented software development,
• What philosophical ontology can add to the debate on the mapping between objects in the real world and system objects,
• What the key obstacles to the deployment of ontology are

Modern business organizations experience increasing challenges in the development and evolution of their enterprise systems. Typical problems include legacy re-engineering, systems integration/interoperability, and the architecting of the enterprise. At the heart of all these problems is enterprise modeling. Many enterprise modeling approaches have been proposed in the literature with some based on ontology. Few however adopt a foundational ontology to underpin a range of enterprise models in a consistent and coherent manner. Fewer still take data-driven re-engineering as their natural starting point for modeling. This is the approach taken by Business Object Reference Ontology (BORO). It has two closely intertwined components: a foundational ontology and a re-engineering methodology. These were originally developed for the re-engineering of enterprise systems and subsequently evolved into approaches to enterprise architecture and systems integration. Together these components are used to systematically unearth reusable and generalized business patterns from existing data. Most of these patterns have been developed for the enterprise context and have been successfully applied in several commercial projects within the financial, defense, and oil and gas industries. BORO's foundational ontology is grounded in philosophy and its metaontological choices (including perdurantism, extensionalism, and possible worlds) follow well-established theories. BORO's re-engineering methodology is rooted in the philosophical notion of grounding; it emerged from the practice of deploying its foundational ontology and has been refined over the last 25 years. This paper presents BORO and its application to enterprise modeling.

An overview of BORO Foundational Ontology’s Meta-ontological Choices. This covers:

• Background - BORO as an extensional ontology for business systems
• The context for metaphysical choices
• How does philosophy characterise the different metaphysics? Metaphysics through the eyes of philosophy textbooks
• BORO’s metaphysical choices
• Top level patterns - that emerge as a result of the choices
• Re-engineering the companies house data - an example of the re-engineering process assocaited with the choices
• Company - an example of the result of the choices
• Higher order types - one of BORO's metaphysical choices

This book is motivated by the belief that “a better understanding of ontology, epistemology, and teleology” is essential for enabling Modelling and Simulation (M&S) systems to reach the next level of ‘intelligence’. This chapter focuses on one broad category of M&S systems where the connection is more concrete; ones where building an ontology – and, we shall suggest, an epistemology – as an integrated part of their design will enable them to reach the next level of ‘intelligence’. Within the M&S community, this use of ontology is at an early stage; so there is not yet a clear picture of what this will look like. In particular, there is little or no guidance on the kind of ontological architecture that is needed to bring the expected benefits. This chapter aims to provide guidance by outlining some major concerns that shape the ontology and the options for resolving them. The hope is that paying attention to these concerns during design will lead to a better quality architecture, and so enable more ‘intelligent’ systems. It is also hoped that understanding these concerns will lead to a better understanding of the role of ontology in M&S.