Research on ontology is becoming increasingly widespread in the computer science community, and its importance is being recognized in a multiplicity of research fields and application areas, including knowledge engineering, database design and integration, information retrieval and extraction. We shall use the generic term information systems, in its broadest sense, to collectively refer to these application perspectives. We argue in this paper that so-called ontologies present their own methodological and architectural peculiarities: on the methodological side, their main peculiarity is the adoption of a highly interdisciplinary approach, while on the architectural side the most interesting aspect is the centrality of the role they can play in an information system, leading to the perspective of ontology-driven information systems.

Composite systems are generally comprised of heterogeneous components whose specifications are developed by many development participants. The requirements of such systems are invariably elicited from multiple perspectives which overlap, complement and contradict each other. Furthermore, these requirements are generally developed and specified using multiple methods and notations respectively. It is therefore necessary to express and check the relationships between the resultant specification fragments. In this paper we deploy multiple "ViewPoints" that hold partial requirements specifications, described and developed using different representation schemes and development strategies. We discuss the notion of interViewPoint communication in the context of this ViewPoints framework, and propose a general model for ViewPoint interaction and integration. We elaborate on some of the requirements for expressing and enacting inter-ViewPoint relationships - the vehicles for consistency che...

The purpose of this paper is to defend the systematic introduction of formal ontological principles in the current practice of knowledge engineering, to explore the various relationships between ontology and knowledge representation, and to present the recent trends in this promising research area. According to the "modelling view" of knowledge acquisition proposed by Clancey, the modeling activity must establish a correspondence between a knowledge base and two separate subsystems: the agent's behavior (i.e. the problem-solving expertize) and its own environment (the problem domain). Current knowledge modelling methodologies tend to focus on the former subsystem only, viewing domain knowledge as strongly dependent on the particular task at hand: in fact, AI researchers seem to have been much more interested in the nature of reasoning rather than in the nature of the real world. Recently, however, the potential value of task-independent knowlege bases (or "ontologies") suitable to large scale integration has been underlined in many ways. In this paper, we compare the dichotomy between reasoning and representation to the philosophical distinction between epistemology and ontology. We introduce the notion of the ontological level, intermediate between the epistemological and the conceptual level discussed by Brachman, as a way to characterize a knowledge representation formalism taking into account the intended meaning of its primitives. We then discuss some formal ontological distinctions which may play an important role for such purpose.

Abstiuct-Organizational databases are pervaded with data of poor quality. However, there has not been an analysis of the data quality literature that provides an overall understanding of the state-of-art research in this area. Using an analogy between product manufacturing and data manufacturing, this paper de-velops a framework for analyzing data quality research, and uses it as the basis for organizing the data quality literature. This framework consists of seven elements: management responsibili-ties, operation and assurance costs, research and development, production, distribution, personnel management, and legal func-tion. The analysis reveals that most research efforts focus on op-eration and assurance costs, research and development, and pro-duction of data products. Unexplored research topics and unre-solved issues are identified and directions for future research provided. Index Terms-Data quality, data manufacturing, data product,

Abstract- Process modeling has gained prominence in the information systems modeling area due to its focus on business processes and its usefulness in such business improvement methodologies as Total Quality Management, Business Process Reengineering. and Workflow Management. However, process modeling techniques are not without their criticisms [13]. This paper proposes and uses the Bunge-Wand-Weber (BWW) representation model to analyze the five views- process, data, function, organization and output- provided in the Architecture of Integrated Information Systems (ARIS) popularized by Scheer [39, 40, 411. The BWW representation model attempts to provide a theoretical base on which to evaluate and thus contribute to the improvement of information systems modeling techniques. The analysis conducted in this paper prompts some propositions. It confirms that the process view alone is not sufficient to model all the real-world constructs required. Some other symbols or views are needed to overcome these deficiencies. However, even when considering all five views in combination, problems may arise in representing all potentially required business rules, specifying the scope and boundaries of the system under consideration, and employing a “top-down” approach to analysis and design. Further work from this study will involve the operationalization of these

Database design commonly assumes, explicitly or implicitly, that instances must belong to classes. This can be termed the assumption of inherent classification. We argue that the extent and complexity of problems in schema integration, schema evolution, and interoperability are, to a large extent, consequences of inherent classification. Furthermore, we make the case that the assumption of inherent classification violates philosophical and cognitive guidelines on classification and is, therefore, inappropriate in view of the role of data modeling in representing knowledge about application domains. As an alternative, we propose a layered appro...

Abstract. The present essay discusses the ontology, epistemology, methodology and ethics of design science. It suggests that Information Systems as a design science should be based on a sound ontology, including an ontology of IT artifacts. In the case of epistemology, the essay emphasizes the irreducibility of the prescriptive knowledge of IT artifacts to theoretical descriptive knowledge. It also expresses a need for constructive research methods, which allow disciplined, rigorous and transparent building of IT artifacts as outcomes of design science research. The relationship between action research and design science research is also briefly discussed. In the case of ethics, the essay points out that Information Systems as design science cannot be valuefree.

Data modelling languages are used in today’s information systems engineering environments. Many have a degree of hype surrounding their quality and applicability with narrow and specific justification often given in support of one over another. We want to more deeply understand the fundamental nature of data modelling languages. We thus propose a theory, based on ontology, that should allow us to understand, compare, evaluate, and strengthen data modelling languages. In this paper we present a method (conceptual evaluation) and its extension (conceptual comparison), as part of our theory. Our methods are largely independent of a specific ontology. We introduce Chisholm’s ontology and apply our methods to analyse some data modelling languages using it. We find a good degree of overlap between all of the data modelling languages analysed and the core concepts of Chisholm’s ontology, and conclude that the data modelling languages investigated reflect an ontology of commonsense-realism.

The increasing interest in process engineering and application integration has resulted in the appearance of various new process modelling languages. Understanding and comparing such languages has therefore become a major problem in information systems research and development. We suggest a framework to solve this problem involving several instruments: a general process metamodel with a table, an analysis of the event concept, and a classification of concepts according to the interrogative pronouns: what, how, why, who, when, and where. This framework can be used for several purposes, such as translating between languages or verifying that relevant organisational aspects have been captured. To validate the framework, three different process modelling languages have been compared: Business Modelling Language (BML), Event-driven Process Chains (EPC) and UML State Diagrams.

Knowledge management research focuses on the development of concepts, methods, and tools supporting the management of human knowledge. The main objective of this paper is to survey some of the basic concepts that have been used in computer science for the representation of knowledge and summarize some of their advantages and drawbacks. A secondary objective is to relate these techniques to information sciences theory and practice. The survey classifies the concepts used for knowledge representation into four broad ontological categories. Static ontology describes static aspects of the world, i.e., what things exist, their attributes and relationships. A dynamic ontology, on the other hand, describes the changing aspects of the world in terms of states, state transitions and processes. Intentional ontology encompasses the world of things agents believe in, want, prove or disprove, and argue about. Social ontology covers social settings, agents, positions, roles, authority, permanent org...