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.

s. The Plinius ontology was developed to support the translation of natural-language sentences into expressions in a knowledge representation language [31]. Those design decisions taken during the development of the ontology which appeared to be domain-independent have been proposed as general ontology development principles. These are: (1) conflicting assertions about the same entity can be more readily discovered if the concepts are defined as fully as possible. (2) pre-existing formal theories are taken as given and a domain ontology does not specify the semantics of logical constants. (3) an ontology should be independent of any particular knowledge representation language. (4) the principle of the conceptual construction kit states that an ontology consists of primitives concepts and construction rules that allow the definition of all other concepts in terms of these primitives. (5) a bottom-up approach is taken in order that the ontology exhibits sufficient completeness for the...

The contributions of this thesis bridge two disciplines: environmental science (specifically, wastewater management) and computer science (specifically, artificial intelligence). Wastewater management as a discipline operates using a range of different approaches and methods which include: manual control, on-line automatic control, numerical or non-numerical models, statistical models and simulation models. The thesis characterizes an interdisciplinary research on artificial intelligence techniques (rule-based reasoning, case-based reasoning, ontologies and planning) applied to environmental decision-support systems. The integrated architecture's design of this application, the OntoWEDSS system, augments classic reasoning systems (rule-based reasoning and case-based reasoning) with a domain ontology about the management of wastewater treatment plants. The integration of the newly created WaWO ontology provides a more flexible management capability to OntoWEDSS. The construction of the OntoWEDSS decision support system is based on a specific case study but the system is also of general interest, given that its ontologyunderpinned architecture can be applied to any wastewater treatment plant and, at an appropriate level of abstraction, to other environmental domains. The OntoWEDSS system improves the diagnosis of the state of a treatment plant, provides support for wastewater-related complex problem-solving, and facilitates knowledge modelling and reuse by means of the WaWO ontology.

Ontology is a crucial component in knowledge management and organizational learning. Despite the widespread ontology-related research and applications, developing domain ontologies remains a challenging issue. Ontology development for underdeveloped domains, which have no structural resources in existence, becomes even more difficult and time-consuming. In this paper, we propose a rapid ontology development (ROD) methodology that can be used to build ontology for underdeveloped domains. It consists of four processes: domain analysis, document and language processing, statistical analysis and extraction, and collaborative mapping. On the one hand, ROD intends to make the development process more efficient so that domain concepts and relations can be automatically discovered from large-scale semi-structured and/or unstructured textual resources. On the other hand, it requires domain experts ’ involvement as is needed with the assistance of a customizable collaborative system. The approach has been assessed using community development domain. In addition to the expected results, we have also made some new observations from the experiment. We believe that ROD can also be applied to established domains.

A knowledge management system must support the integration of information from disparate sources, wherein a decision maker manipulates information that someone else conceptualised and represented. So the system must minimise ambiguity and imprecision in interpreting shared information. This can be achieved by representing the shared information using ontologies. There are typically two approaches to developing ontologies to support decision making. In one approach, ontologies are developed to support new business processes or decisions, but often are not built from existing data repositories. In the other approach, ontologies are developed from existing data repositories, but often may not support new business processes and decisions. In this paper, a methodology for knowledge management that combines both process and data driven approaches to ontology development and builds on them is described. In this methodology, called the BPD/D Ontological Engineering Methodology, competency questions that state the capability of an ontology to support business processes and decisions are specified. Concurrently, architectural requirements that specify aspects of existing systems that constrain ontology design choices are also stated, so that existing data repositories are explicitly considered and built upon when developing ontologies. Information systems tools to support both ontology-based knowledge management system construction and use can then be designed to support the steps of this methodology.

Introduction Describing software entities using Semantic Web technology is a growing research area. Our work in estigates the semantic description of software entities that pro ide an application programmer's interface (API) to allow reasoning with the descriptions and their interrelationships. We present an ontology for our semantic middleware, called Application Ser er for the Semantic Web[3], where it is used to facilitate implementation tasks and semantic disco ery. Building on an emerging standard from the Semantic Web community, our work includes a number of extensions to DAML-S [1] which currently allows semantic description of a particular type of software entities, iz. web-serices, in order to facilitate their automatic disco ery and integration. 2 The Ontology Although DAML-S ser es as a good starting point for our ontology, the main di#culty was in the type of software entities to be described. While DAMLS describes web-serices, our goal is to describe softw

Diplomarbeit Semi-automatic ontology engineering and ontology supported document indexing in a multilingual environment Heimatanschrift: