This tutorial presents an introduction to the BORO methodology, an ontology-based systems engineering approach. The authors present both the ontological foundations of the approach as well as business examples of the application of this approach.

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 software stability approach is required to balance the seemingly contradictory goals of stability over the software lifecycle with the need for adaptability, extensibility and interoperability. This workshop paper addresses the issue of how software stability can be achieved over time by outlining an approach to evolving General Business Patterns (GBPs) from the empirical data contained within legacy systems. GBPs are patterns of business objects that are (directionally) stable across contexts of use. The work explains, via a small worked example, how stability is achieved via a process of ‘sophistication’. The outcome of the process demonstrates how the balance that stability seeks can be achieved.

The amount of data that is being made available on the Web is increasing. This provides business organisations with the opportunity to acquire large datasets in order to offer novel information services or to better market existing products and services. Much of this data is now publicly available (e.g., thanks to initiatives such as Open Government Data). The challenge from a corporate perspective is to make sense of the third party data and transform it so that it can more easily integrate with their existing corporate data or with datasets with a different provenance. This paper presents research-in-progress aimed at semantically transforming raw data on U.K. registered companies. The approach adopted is based on BORO (a 4D foundational ontology and re-engineering method) and the target technological platform is Neo4J (a graph database). The primary challenges encountered are (1) re-engineering the raw data into a 4D ontology and (2) representing the 4D ontology into a graph database. The paper will discuss such challenges and explain the transformation process that is currently being adopted.

Information systems (IS) are getting larger and more complex, becoming ‘gargantuan’. IS practices have not evolved in step to handle the development and maintenance of these gargantuan systems, leading to a variety of quality issues. The community recognises that they need to develop an appropriate organising architecture and are making significant efforts. Examples include the System Engineering Modeling Language (SysML), the Reference Model for Open Distributed Processing (RM-ODP) and 4+1 Architectural Blueprints. Most of these follow IEEE 1471-2000’s recommendation to use view models. We believe that these efforts are missing a key component – an information grounding view. In this paper, we firstly describe this view. Then we suggest a way to provide an architecture for it – foundational ontologies – and a way of assessing them – metaphysical choices. We illustrate how the metaphysical choices are made and how this can affect information modelling.

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.

Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move toward formalization in part because it illustrates some of the barriers to formalization, including the formal complexity of the pattern and the ontological issues surrounding the “one and the many.” Powersets are a way of characterizing the (complex) formal structure of the classification pattern, and their formalization has been extensively studied in mathematics since Cantor’s work in the late nineteenth century. One can use this formalization to develop a useful benchmark. There are various communities within information systems engineering (ISE) that are gradually working toward a formalization of the classification pattern. However, for most of these communities, this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other information systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design, and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature, starting from the relevant theoretical works of the mathematical literature and gradually shifting focus to the ISE literature. The literature survey follows the evolution of ISE’s understanding of how to formalize the classification pattern. The various proposals are assessed using the classical example of classification; the Linnaean taxonomy formalized using powersets as a benchmark for formal expressiveness. The broad conclusion of the survey is that (1) the ISE community is currently in the early stages of the process of understanding how to formalize the classification pattern, particularly in the requirements for expressiveness exemplified by powersets, and (2) that there is an opportunity to intervene and speed up the process of adoption by clarifying this expressiveness. Given the central place that the classification pattern has in domain modeling, this intervention has the potential to lead to significant improvements.

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.

With re-engineering of software systems becoming quite pronounced amongst organisations, a software stability approach is required to balance the seemingly contradictory goals of stability over the software lifecycle with the need for adaptability, extensibility and interoperability. This paper addresses the issue of how software stability can be achieved over time by outlining an approach to evolving General Business Patterns (GBPs) from the empirical data contained within legacy systems. GBPs are patterns of business objects that are (directionally) stable across contexts of use. The approach is rooted in developing patterns by extracting the business knowledge embedded in existing software systems. The process of developing this business knowledge is done via the careful use of ontology, which provides a way to reap the benefits of clear semantic expression. A worked example is presented to show how stability is achieved via a process of ‘interpretation’ and ‘sophistication’. The outcome of the process demonstrates how the balance that stability seeks can be achieved.

Several foundational ontologies have been developed recently. We examine two of these from the point of view of their quality in representing temporal changes, focusing on the example of roles. We discuss how these are modelled in two foundational ontologies: the Unified Foundational Ontology and the BORO foundational ontology. These exhibit two different approaches, endurantist and perdurantist respectively. We illustrate the differences using a running example in the university student domain, wherein one individual is not only a registered student but also, for part of this period, was elected the President of the Student Union. The metaphysical choices made by UFO and BORO lead to different representations of roles. Two key differences which affect the way roles are modelled are exemplified in this paper: (1) different criteria of identity and (2) differences in the way individual objects extend over time and possible worlds. These differences impact upon the quality of the models produced in terms of their respective explanatory power. The UFO model concentrates on the notion of validity in “all possible worlds” and is unable to accurately represent the way particulars are extended in time. The perdurantist approach is best able to describe temporal changes wherein roles are spatio-temporal extents of individuals.