The word integration has been used with different meanings in the ontology field. This article aims at clarifying the meaning of the word "integration" and presenting some of the relevant work done in integration. We identify three meanings of ontology "integration": when building a new ontology reusing (by assembling, extending, specializing or adapting) other ontologies already available; when building an ontology by merging several ontologies into a single one that unifies all of them; when building an application using one or more ontologies. We discuss the different meanings of "integration", identify the main characteristics of the three different processes and propose three words to distinguish among those meanings: integration, merge and use.

In the last years, the development of ontology-based applications has increased considerably, mainly related to the semantic web. Users currently looking for ontologies in order to incorporate them into their systems, just use their experience and intuition. This makes it difficult for them to justify their choices. Mainly, this is due to the lack of methods that help the user to determine which are the most appropriate ontologies for the new system. To solve this deficiency, the present work proposes a method, ONTOMETRIC, which allows the users to measure the suitability of existing ontologies, regarding the requirements of their systems.

Ontologies and problem-solving methods are promising candidates for reuse in Knowledge Engineering. Ontologies define domain knowledge at a generic level, while problem-solving methods specify generic reasoning knowledge. Both type of components can be viewed as complementary entities that can be used to configure new knowledge systems from existing, reusable components. In this paper, we give an overview of approaches for ontologies and problem-solving methods. 1 Introduction In 1991, the ARPA Knowledge Sharing Effort [NFF 91] envisioned a new way in which intelligent systems could be built. They proposed the following: "Building knowledgebased systems today usually entails constructing new knowledge bases from scratch. It could be done by assembling reusable components. Systems developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This new system would interoperate with existing systems, us...

Early work on the NONLIN and O-Plan projects indicated a need for a defined methodology which would guide users performing various roles in the acquisition and analysis of domain requirements for planning. This work included links to a requirement analysis methodology, CORE (COntrolled Requirements Expression) , tool support via an intelligent assistant as part of the Task Formalism (TF) Workstation and an initial collection of guidelines and checklists to aid in using the TF domain description language. This paper describes work underway to follow-on from this past research and to infuse it with knowledge gained from recent research related to planning domain development, knowledge modelling, design rationale and ontological and requirements engineering. Introduction The activities involved in discovering, engineering, documenting, and maintaining a set of domain constructs for most AI planning-based projects can be considered ad hoc and disorganised, at best. The current sources for...

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INTRODUCTION In 1991, the ARPA Knowledge Sharing Eort (Neches et al., 91) revolutionized the way in which intelligent systems were built. They proposed the following: \Building Knowledge-based systems today usually entails constructing new knowledge bases from scratch. It could be done by assembling reusable components. Systems developers would then only need to worry about creating the specialized knowledge and reasoners new to the specic task of their system, using them to perform some of its reasoning. In this way, declarative knowledge, problem-solving techniques and reasoning services would all be shared among systems. This approach would facilitate building bigger and better systems cheaply..." Since them, considerable progress has been made in developing the conceptual bases for building technology that allows knowledge-component reuse and sharing. To enable sharing and reuse of knowledge and reasoning behavior across domains and tasks, Ontologies and Problem Solving

This paper presents and motivates an extended ontology knowledge model which represents semantic information about concepts explicitly. This knowledge model results from enriching the standard conceptual model with semantic information which precisely characterises the concept's properties and expected ambiguities, including which properties are prototypical of a concept and which are exceptional, the behaviour of properties over time and the degree of applicability of properties to subconcepts. This enriched conceptual model permits a precise characterisation of what is represented by class membership mechanisms and helps knowledge engineers to determine, in a straightforward manner, the meta-properties holding for a concept. Meta-properties are recognised to be the main tool for a formal ontological analysis that allows building ontologies with a clean and untangled taxonomic structure. This enriched semantics can prove useful to describe what is known by agents in a multi-agent systems, as it facilitates the use of reasoning mechanisms on the knowledge that instantiate the ontology. These mechanisms can be used to solve ambiguities that can arise when heterogeneous agents have to interoperate in order to perform a task.

A methodology for introducing an ontology-based KM solution into enterprises is described which extends and improves the CommonKADS methodology by introducing --- among others --- specific guidelines for developing and maintaining the respective ontology. Special emphasis is put on a stepwise construction and evaluation of the ontology. The methodology is supported by a tool, "OntoKick", that supports ontology engineers in early stages, i.e. the kickoff phase of ontology development. Keywords. Knowledge Management, Methodology, Ontology 1

Introduction In recent years Knowledge Management (KM) has become an important success factor for enterprises. Increasing product complexity, globalization, virtual organizations or customer orientation are developments that ask for a thorough and systematic management of knowledge - within an enterprise and between several cooperating enterprises. Obviously, KM is a major issue for human resource management, enterprise organization and enterprise culture - nevertheless, information technology (IT) plays the crucial enabler for many aspects of KM. As a consequence, KM is an inherently interdisciplinary subject. IT-supported KM solutions are built around some kind of organizational memory [ABH+98] that integrates informal, semi-formal and formal knowl- edge in order to facilitate its access, sharing and reuse by members of the organization(s) for solving their individual or collective tasks [DCGR99]. In such a context, knowledge has to be modelled, appropriately structured and interli

This document is part of a research project partially funded by the IST Programme of the Commission of the European Communities as project number IST-1999-10132. The partners in this project are: Vrije Universiteit Amsterdam VUA (coordinator, NL), University of Karlsruhe (Germany), Schweizerische Lebensversicherungs- und Rentenanstalt/Swiss Life (Switzerland), British Telecommunications plc (UK), CognIT a.s. (Norway), EnerSearch AB (Sweden), AIdministrator Nederland BV (NL)