8,070 research outputs found

    Some Ideas and Examples to Evaluate Ontologies

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    The lack of methods for evaluating ontologies in laboratories can be an obstacle to their use in companies. This paper presents a set of emerging ideas in evaluation of ontologies useful for: (1) ontologies developers in the lab, as a foundation from which to perform technical evaluations; (2) end users of ontologies in companies, as a point of departure in the search for the best ontology for their systems; and (3) future research, as a basis upon which to perform progressive and disciplined investigations in this area. After briefly exploring some general questions such as: why, what, when, how and where to evaluate; who evaluates; and, what to evaluate against, we focus on the definition of a set of criteria useful in the evaluation process. Finally, we use some of these criteria in the evaluation of the Bibliographic-Data [5] ontology

    Integrating e-commerce standards and initiatives in a multi-layered ontology

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    The proliferation of different standards and joint initiatives for the classification of products and services (UNSPSC, e-cl@ss, RosettaNet, NAICS, SCTG, etc.) reveals that B2B markets have not reached a consensus on the coding systems, on the level of detail of their descriptions, on their granularity, etc. This paper shows how these standards and initiatives, which are built to cover different needs and functionalities, can be integrated in an ontology using a common multi-layered knowledge architecture. This multi-layered ontology will provide a shared understanding of the domain for applications of e-commerce, allowing the information sharing between heterogeneous systems. We will present a method for designing ontologies from these information sources by automatically transforming, integrating and enriching the existing vocabularies with the WebODE platform. As an illustration, we show an example on the computer domain, presenting the relationships between UNSPSC, e-cl@ss, RosettaNet and an electronic catalogue from an e-commerce platform

    BAREMO: How to Choose the Appropriate Software Component Using the Analytic Hierarchy Process

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    To select a software component from several similar candidates is a complex task, since each project pursues different objectives. We intend to use the Analytic Hierarchy Process in the taking of multicriteria decisions for software component reuse. This method is called BAREMO. It will help the software engineer to make estimations which will enable him/her to choose the appropriate component. The article presents a case study of the application of the method, where a project manager assesses a certain software component in order to consider its reuse in the domain of image processing

    Evaluating Knowledge Representation and Reasoning Capabilites of Ontology Specification Languages

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    The interchange of ontologies across the World Wide Web (WWW) and the cooperation among heterogeneous agents placed on it is the main reason for the development of a new set of ontology specification languages, based on new web standards such as XML or RDF. These languages (SHOE, XOL, RDF, OIL, etc) aim to represent the knowledge contained in an ontology in a simple and human-readable way, as well as allow for the interchange of ontologies across the web. In this paper, we establish a common framework to compare the expressiveness of "traditional" ontology languages (Ontolingua, OKBC, OCML, FLogic, LOOM) and "web-based" ontology languages. As a result of this study, we conclude that different needs in KR and reasoning may exist in the building of an ontology-based application, and these needs must be evaluated in order to choose the most suitable ontology language(s)

    The Artificial Intelligence Course at the Faculty of Computer Science in the Polytechnic University of Madrid

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    This paper presents the experience of teaching an Artificial Intelligence course at the Faculty of Computer Science in the Polytechnic University of Madrid, Spain. The objective of this course is to introduce the students to this field, to prepare them to contribute to the evolution of the technology, and to qualify them to solve problems in the real world using Artificial Intelligence technology. The curriculum of the Artificial Intelligence course, which is integrated into the Artificial Intelligence Department's program, allows us to educate the students in this sense using the monographic teaching method

    WebPicker: Knowledge Extraction from Web Resources

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    We show how information distributed in several web resources and represented in different restricted languages can be extracted from its original sources and transformed into a common knowledge model represented in XML using WebPicker. This information, which has been built to cover different needs and functionalities, can be later imported into WebODE, integrated, enriched and exported into different representation formats using WebODE specific modules. We show a case study in the e-commerce domain, using products and services standards from several organizations and/or joint initiatives of industrial and services companies, and a product catalogue from an e-commerce platform

    Guidelines to Study Differences in Expressiveness between Ontology Specification Languages: A Case Of Study

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    We focus on our experiences on translating ontologies between two ontology languages, FLogic and Ontolingua, in the framework of Methontology and ODE. Rather than building "ad hoc" translators between languages or using KIF, our option consists of translating through ODE intermediate representations. So, we have built direct translators from ODE intermediate representations to Ontolingua and FLogic, and we have also built reverse translators from these two languages to ODE intermediate representations. Expressiveness of the target languages is the main feature to analyse when automatically generating ontologies from ODE intermediate representations. Therefore, we analyse the expressiveness of Ontolingua and FLogic for creating classes, instances, relations, functions and axioms, which are the essential components in ontologies. The motivation for this analysis can be found in the (KA)² initiative and can be easily extended to any other domains and languages

    What is the current state of the Multilingual Web of Data?

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    The Semantic Web is growing at a fast pace, recently boosted by the creation of the Linked Data initiative and principles. Methods, standards, techniques and the state of technology are becoming more mature and therefore are easing the task of publication and consumption of semantic information on the Web

    SGSDesigner, the ODESGS Environment User Interface

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    In this demo, we will show SGSDesigner, the ODESGS Environment user interface. ODESGS Environment (the realization of the ODESGS Framework [1]) is an environment for supporting both a) the annotation of pre-existing Grid Services(GSs) and b) the design of new complex Semantic Grid Services(SGSs) in a (semi) automatic way. In the demo we will focus in the annotation of a WSRF GS, using the annotation process proposed by the ODESGS Framework

    Ontological Reengineering for Reuse

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    This paper presents the concept of Ontological Reengineering as the process of retrieving and transforming a conceptual model of an existing and implemented ontology into a new, more correct and more complete conceptual model which is reimplemented. Three activities have been identified in this process: reverse engineering, restructuring and forward engineering. The aim of Reverse Engineering is to output a possible conceptual model on the basis of the code in which the ontology is implemented. The goal of Restructuring is to reorganize this initial conceptual model into a new conceptual model, which is built bearing in mind the use of the restructured ontology by the ontology/application that reuses it. Finally, the objective of Forward Engineering is output a new implementation of the ontology. The paper also discusses how the ontological reengineering process has been applied to the Standard-Units ontology [18], which is included in a Chemical-Elements [12] ontology. These two ontologies will be included in a Monatomic-Ions and Environmental-Pollutants ontologies
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