Describes the development of the BORO-IDEAS based MODEM, a semantic metamodel for MODAF.

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.

Data integration of enterprise systems typically involves combining heterogeneous data residing in different sources into a unified, homogeneous whole. This heterogeneity takes many forms and there are all sorts of significant practical and theoretical challenges to managing this, particularly at the semantic level. In this paper, we consider a type of semantic heterogeneity that is common in Model Driven Architecture (MDA) Computation Independent Models (CIM); one that arises due to the data’s dependence upon the system it resides in. There seems to be no relevant work on this topic in Conceptual Modelling, so we draw upon research done in philosophy and linguistics on formalizing pure indexicals – ‘I’, ‘here’ and ‘now’ – also known as de se (Latin ‘of oneself’) or the deitic centre. This reveals firstly that the core dependency is essential when the system is agentive and the rest of the dependency can be designed away. In the context of MDA, this suggests a natural architectural layering; where a new concern ‘system dependence’ is introduced and used to divide the CIM model into two parts; a system independent ontology model and a system dependent agentology model. We also show how this dependence complicates the integration process – but, interestingly, not reuse in the same context. We explain how this complication usually provides good pragmatic reasons for maximizing the ontology content in an ‘Ontology First’, or ‘Ontology then Agentology’ approach.

One of the central concerns of the multi-level modelling (MLM) community is the hierarchy of classifications that appear in conceptual models; what these are, how they are linked and how they should be organised into levels and modelled. Though there has been significant work done in this area, we believe that it could be enhanced by introducing a systematic way to investigate the ontological nature and requirements that underlie the frameworks and tools proposed by the community to support MLM (such as Orthogonal Classification Architecture and Melanee). In this paper, we introduce a key component for the investigation and understanding of the ontological requirements, an ontological sandbox. This is a conceptual framework for investigating and comparing multiple variations of possible ontologies – without having to commit to any of them – isolated from a full commitment to any foundational ontology. We discuss the sandbox framework as well as walking through an example of how it can be used to investigate a simple ontology. The example, despite its simplicity, illustrates how the constructional approach can help to expose and explain the metaphysical structures used in ontologies, and so reveal the underlying nature of MLM levelling.

The workshop aimed at providing a forum to discuss the use of philosophical ontology in object-oriented information systems. Whilst ontology is now more widely used in computing circles – knowledge representation, system integration, legacy transformation, and the semantic web for example – initial attempts have been modest in their outcomes. This is because computing ontology to-date has been used primarily for (often competing) concept definitions: Pragmatically, ontologies have either been developed in an abstract sense (based on some authorative perspective), or people have taken materials at hand (data models and the like) and tried to glue them together. A sound basis on which to properly align different views on aspects of the world in order to work towards a consistent whole is missing. With this in mind, the workshop aimed to secure a measure of agreement on:

• What philosophical ontology is,
• How ontology can assist in software development,
• Key obstacles to the deployment of ontology, and
• Possible collaborative efforts among the participants.

Selection of participants was based on short position papers and/or previously demonstrated interest in related areas of activity.
The title of this report should be referenced as “Report from the ECOOP 2004 Workshop on Philosophy, Ontology, and Information Systems”.

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

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 paper presents a report on work in progress of a Synthesis of (selected) State of the Art Enterprise Ontologies (SSAEO) – which aims to produce a Base Enterprise Ontology to be used as the foundation for the construction of a Core Enterprise Ontology (CEO). The synthesis is intended to harvest the insights from the selected ontologies, building upon their strengths and eliminating – as far as possible – their weaknesses. One of the main achievements of this work is the development of the notion of a person (entities that can acquire rights and obligations) enabling the integration of a number of lower level concepts. In addition, we have already been able to identify some of the common ‘mistakes’ in current enterprise ontologies – and propose solutions.

This paper is a report on progress of the CEO project whose goal is to build a state of the art enterprise ontology. The project is currently at the stage of harvesting insights from the best existing enterprise ontologies. The goal of this stage is to synthesise from these a Base Enterprise Ontology. This will then be used as the foundation for the construction of the ‘industrial strength’ Core Enterprise Ontology (CEO). The synthesis is intended to build upon the strengths and eliminating — as far as possible — the weaknesses from the selected ontologies. Among other things, this paper describes one of the main achievements of this work to date: the development of the notion of a person (entities that can acquire rights and obligations) enabling the integration of a number of lower level concepts. In addition, it identifies some of the common ‘mistakes’ in current enterprise ontologies — and proposes solutions.

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.

This is the second part of an introduction to a series of tutorials that aim to provide a practical introduction for researchers and practitioners to potential for the use of foundational ontologies in industrial applications, based upon an actual application. These tutorials will be based upon industrial work currently being done using the BORO foundational methodology; an ontology-based systems (and data) re-engineering and modernisation approach. The first tutorial tutorial introduced ontology, particularly foundational ontologies. This second part introduces the BORO ontology.

The subsequent tutorials will use this introductory tutorial as a basis, they will walk through a number of illustrative examples of how the BORO methodology has been used to re-engineer data in an industrial context.

A major challenge faced in the deployment of collaborating unmanned vehicles is enabling the semantic interoperability of sensor data. One aspect of this, where there is significant opportunity for improvement, is characterizing the coordinate systems for sensed position data. We are involved in a proof of concept project that addresses this challenge through a foundational conceptual model using a constructional approach based upon the BORO Foundational Ontology. The model reveals the characteristics as sets of options for configuring the coordinate systems. This paper examines how these options involve, ontologically, ascending levels. It identifies two types of levels, the well-known type levels and the less wellknown tuple/relation levels.