18 research outputs found

    Design issues for agent-based resource locator systems

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    While knowledge is viewed by many as an asset, it is often difficult to locate particularitems within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work presented complements current research into both expertise finders and recommender systems. The essential issues for the proposed design are scalability, together ith the ability to learn and adapt to changing resources. As knowledge is often implicit within electronic resources, and therefore difficult to locate, we have proposed the use of ontologies, to extract the semantics and infer meaning to obtain the results required. We explore the use of communities of practice, applying ontology-based networks, and e-mail message exchanges to aid the resource discovery process

    An Ontology-centered Approach for Designing an Interactive Competence Management System for IT Companies

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    The paper presents a generic framework for an intelligent information system of competence management based on ontologies for information technology companies. In a first step it will be applied in an information technology (IT) small enterprise and then its applicability will be verified for other organizations of the same type. The work presented in the paper is performed under the project "CONTO – Ontology-based Competencies Management in Information Technology" funded by the Romanian Ministry of Education and Research, involving two universities, a research institute and an IT private company. A competence management system (CMS), in our vision has to achieve three functions: (a) to support the complete and systematic acquisition of knowledge about the competence of the members of an enterprise; (b) to provide the knowledge about competences and their owners; (c) to apply the available knowledge to serve a purpose. The core of the competence management information system is an ontology that plays the role of the declarative knowledge repository containing the basic concepts (such as: company-job, competence, domain, group, person etc.) and their relationships with other concepts, instances and properties. The Protégé environment was used for the development of this ontology. The structure of the ontology is conceived so that description logics can be used to represent the concept definitions of the application domain in a structured and formally well-understood way. Knowledge acquisition is performed in our approach by enriching the ontology, according to the requirements of the IT company. An advantage of using an ontology-based system is the possibility of the identification of new relations among concepts based on inferences starting from the existing knowledge. The user can choose to query instances of one type of concept. The paper also presents some use-cases

    A Competence Management System for Universities

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    UNICOMP: Identification of Enterprise Competencies to Build Collaborative Networks

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    Abstract. In a context of decision-aid to support the identification of collaborative networks, this paper focuses on extracting essential facets of firm competencies. Due to the complexity of the notion of competence, this contribution is based on a semantic representation of information using semantic ontology, bonds and a linguistic treatment based on the utilization of syntactic patterns. To identify enterprise competencies, the UNICOMP system uses company web sites as information source, as well as a general ontology of competencies as semantic resource

    A competence management system towards increased corporate success

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    Estágio realizado na Critical Manufacturing, S. ATese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

    SELECTION CRITERION AND IMPLEMENTATION OF CASE TOOLS IN GAP ANALYSIS TOWARDS DISTRIBUTED SOFTWARE DEVELOPMENT IJCET © I A E M E

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    ABSTRACT Software is growing ever-more complex and new software processes, methods and products put greater demands on software engineers than ever before. Software Engineering is broadly associated with the development of quality software with increasing use of software preparation standards and guidelines. Organizations now have a tendency to make greater efforts in developing software in distributive way. The main advantage of this lies in a greater availability of human resources in decentralized zones at less cost. There are, however, some disadvantages which are caused by the distance that separates the development teams. Coordination and communication among the team members become more difficult as the software components are sourced from different places, thus affecting project organization, project control, and product quality. In addition to these, there are major challenges like technical diversities such as hardware and software configuration of distributed site, product architecture, development methodology, managerial techniques result with "gaps" at different levels of distributed software development project. There are different methodologies to deal with these gaps. The use of Computer Aided Software Engineering (CASE) tools has been marketed as a remedy for the software development crisis by automating analysis, design, and coding for the ease the distributed development of software systems. This paper proposes the selection of appropriate CASE tools and their implementation in analysis gaps towards managing distributed software development

    Domain ontology learning from the web

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    El Aprendizaje de Ontologías se define como el conjunto de métodos utilizados para construir, enriquecer o adaptar una ontología existente de forma semiautomática, utilizando fuentes de información heterogéneas. En este proceso se emplea texto, diccionarios electrónicos, ontologías lingüísticas e información estructurada y semiestructurada para extraer conocimiento. Recientemente, gracias al enorme crecimiento de la Sociedad de la Información, la Web se ha convertido en una valiosa fuente de información para casi cualquier dominio. Esto ha provocado que los investigadores empiecen a considerar a la Web como un repositorio válido para Recuperar Información y Adquirir Conocimiento. No obstante, la Web presenta algunos problemas que no se observan en repositorios de información clásicos: presentación orientada al usuario, ruido, fuentes no confiables, alta dinamicidad y tamaño abrumador. Pese a ello, también presenta algunas características que pueden ser interesantes para la adquisición de conocimiento: debido a su enorme tamaño y heterogeneidad, se asume que la Web aproxima la distribución real de la información a nivel global. Este trabajo describe una aproximación novedosa para el aprendizaje de ontologías, presentando nuevos métodos para adquirir conocimiento de la Web. La propuesta se distingue de otros trabajos previos principalmente en la particular adaptación de algunas técnicas clásicas de aprendizaje al corpus Web y en la explotación de las características interesantes del entorno Web para componer una aproximación automática, no supervisada e independiente del dominio. Con respecto al proceso de construcción de la ontologías, se han desarrollado los siguientes métodos: i) extracción y selección de términos relacionados con el dominio, organizándolos de forma taxonómica; ii) descubrimiento y etiquetado de relaciones no taxonómicas entre los conceptos; iii) métodos adicionales para mejorar la estructura final, incluyendo la detección de entidades con nombre, atributos, herencia múltiple e incluso un cierto grado de desambiguación semántica. La metodología de aprendizaje al completo se ha implementado mediante un sistema distribuido basado en agentes, proporcionando una solución escalable. También se ha evaluado para varios dominios de conocimiento bien diferenciados, obteniendo resultados de buena calidad. Finalmente, se han desarrollado varias aplicaciones referentes a la estructuración automática de librerías digitales y recursos Web, y la recuperación de información basada en ontologías.Ontology Learning is defined as the set of methods used for building from scratch, enriching or adapting an existing ontology in a semi-automatic fashion using heterogeneous information sources. This data-driven procedure uses text, electronic dictionaries, linguistic ontologies and structured and semi-structured information to acquire knowledge. Recently, with the enormous growth of the Information Society, the Web has become a valuable source of information for almost every possible domain of knowledge. This has motivated researchers to start considering the Web as a valid repository for Information Retrieval and Knowledge Acquisition. However, the Web suffers from problems that are not typically observed in classical information repositories: human oriented presentation, noise, untrusted sources, high dynamicity and overwhelming size. Even though, it also presents characteristics that can be interesting for knowledge acquisition: due to its huge size and heterogeneity it has been assumed that the Web approximates the real distribution of the information in humankind. The present work introduces a novel approach for ontology learning, introducing new methods for knowledge acquisition from the Web. The adaptation of several well known learning techniques to the web corpus and the exploitation of particular characteristics of the Web environment composing an automatic, unsupervised and domain independent approach distinguishes the present proposal from previous works.With respect to the ontology building process, the following methods have been developed: i) extraction and selection of domain related terms, organising them in a taxonomical way; ii) discovery and label of non-taxonomical relationships between concepts; iii) additional methods for improving the final structure, including the detection of named entities, class features, multiple inheritance and also a certain degree of semantic disambiguation. The full learning methodology has been implemented in a distributed agent-based fashion, providing a scalable solution. It has been evaluated for several well distinguished domains of knowledge, obtaining good quality results. Finally, several direct applications have been developed, including automatic structuring of digital libraries and web resources, and ontology-based Web Information Retrieval
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