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.

The task of information extraction can be seen as a problem of semantic matching between a user-defined template and a piece of information written in natural language. To this purpose, the ontological assumptions of the template need to be suitably specified, and compared with the ontological implications of the text. So-called "ontologies", consisting of theories of various kinds expressing the meaning of shared vocabularies, begin to be used for this task. This paper addresses the theoretical issues related to the design and use of such ontologies for purposes of information retrieval and extraction. After a discussion on the nature of semantic matching within a model-theoretical framework, we introduce the subject of Formal Ontology, showing how the notions of parthood, integrity, identity, and dependence can be of help in understanding, organizing and formalizing fundamental ontological distinctions. We present then some basic principles for ontology design, and we illustrate a preliminary proposal for a top-level ontology develped according to such principles. As a concrete example of ontology-based information retrieval, we finally report an ongoing experience of use of a large linguistic ontology for the retrieval of object-oriented software components.

In their paper on "Using Explicit Ontologies in KBS Development", van Heijst and colleagues seem to take for granted Bylander and Chandrasekaran 's hypothesis on the strong dependence of knowledge represesentation on the nature and the inference strategy of the problem at hand, the socalled interaction problem: Representing knowledge for the purpose of solving some problem is strongly affected by the nature of the problem and the inference strategy to be applied to the problem. [Bylander and Chandrasekaran 1988] The fact that the van Heijst and colleagues don't attempt to explore in detail the arguments sustaining this hypothesis is particularly puzzling, since they admit that it contradicts one of the main assumptions of their well-known KADS approach [Schreiber et al. 1993], namely the separation between domain knowledge and problem-solving knowledge. They report two reasons brought by Bylande

. Much of the work on ontologies in AI has focused on describing some aspect of reality: objects, relations, states of affairs, events, and processes in the world. A goal is to make knowledge sharable, by encoding domain knowledge using a standard vocabulary based on the ontology. A parallel attempt at identifying the ontology of problem-solving knowledge has a goal of sharable problem-solving methods. For example, when one is dealing with abductive inference problems, the following are some of the terms that occur in the representation of problem-solving methods: hypotheses, explanatory coverage, evidence, likelihood, plausibility, composite hypothesis, etc. Method ontology is, in good part, goal- and method-specific. Generic Tasks," Heuristic Classification," Task-specific Architectures," Task-method Structures," Inference Structures" and Task Structures" are representative bodies of work in the knowledge-systems area that have focused on domainindependent problem-solving methods. Ho...

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The paper describes the MIKE (Model-based and Incremental Knowledge Engineering) approach for developing knowledge-based systems. MIKE integrates semiformal and formal specification techniques together with prototyping into a coherent framework. All activities in the building process of a knowledge-based system are embedded in a cyclic process model. For the semiformal representation we use a hypermedia-based formalism which serves as a communication basis between expert and knowledge engineer during knowledge acquisition. The semiformal knowledge representation is also the basis for formalization, resulting in a formal and executable model specified in the Knowledge Acquisition and Representation Language (KARL). Since KARL is executable, the model of expertise can be developed and validated by prototyping. A smooth transition from a semiformal to a formal specification and further on to design is achieved because all the description techniques rely on the same conceptual model to des...

In recent years two main technologies for knowledge sharing and reuse have emerged: ontologies and problem solving methods (PSMs). Ontologies specify reusable conceptualizations which can be shared by multiple reasoning components communicating during a problem solving process. PSMs describe in a domain-independent way the generic reasoning steps and knowledge types needed to perform a task. Typically PSMs are specified in a task-specific fashion, using modelling frameworks which describe their control and inference structures as well as their knowledge requirements and competence. In this paper we discuss a novel approach to PSM specification, which is based on the use of formal ontologies. In particular our specifications abstract from control, data flow and other dynamic aspects of PSMs to focus on the logical theory associated with a PSM (method ontology). This approach concentrates on the competence and knowledge requirements of a PSM, rather than internal control de...

Enforcing ontologies as conceptualisation standards in distributed architectures has some important drawbacks. It is, for instance, no solution for the legacy problem. In this article we address the possibility of accepting multiple heterogeneous ontologies in a distributed architecture. After discussing alternative ways to relate several ontologies we look into one particular (hierarchical) configuration of heterogeneous ontologies and examine how information can passed between individual systems that are linked to different ontologies. 1. Introduction A commonly adopted approach to the integration of (heterogeneous) information systems is to define one shared domain ontology and relate the individual information systems to that shared ontology. One way to do this is to enforce the shared ontology as the standard ontology for all participating systems, thus effectively removing heterogeneity. This approach is usually not feasible because the systems to be integrated are already...

The World-Wide Web is changing the nature of software development to a distributive plug & play process. This requires a new way of managing software by so-called intelligent software brokers. The aim of the European IBROW3 project is to develop an intelligent brokering service that enables third party knowledge-component reuse through the World-Wide Web. Suppliers provide libraries of knowledge components adhering to some standard, and customers can consult these libraries -- through intelligent brokers -- to configure a knowledge system suited to their needs by selection and adaptation. IBROW3 integrates research on heterogeneous databases, interoperability and web technology with knowledge-system technology and ontologies. The aim is to develop a broker that can handle web requests for classes of knowledge system (e.g. diagnostic systems) by accessing libraries of reusable problem-solving methods on the Web, and selecting, adapting and configuring these methods in accor...

Ontology reuse is turning into an important research issue in the ontology field. Ontology reuse can be seen from two points of view: (1) assembling, extending, specializing, adapting other ontologies which are parts of the resulting ontology, or (2) merging different ontologies on the same or similar subject into a single one that unifies all of them. The first kind of reuse is named integration and is the central issue of this paper. In this article, we characterize the integration process, describe and discuss the activities that compose this process and propose an integration methodology. This integration methodology has successfully been applied to build two ontologies in different domains by reusing publicly available ontologies.