TENCompetence: Construyendo la Red Europea para el Desarrollo Continuo de Competencias

Burgos, D., Herder, E., & Olmedilla, D. (2007). TENCompetence: Construyendo la Red Europea para el Desarrollo Continuo de Competencias. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial (AEPIA).El proyecto TENCompetence (The European Network for Lifelong Competence Development) apoya a personas e instituciones europeas en el desarrollo de competencias profesionales más allá de la formación reglada oficial. El desarrollo de habilidades específicas y competencias laborales que enriquecen un curriculum y mejoran la valoración del individuo y sus capacidades profesionales centran el núcleo del proyecto. Como tal, existen dos áreas de trabajo principales: por un lado la implementación e integración de una estructura de servicios; por otro, la investigación de nuevas soluciones y técnicas a los problemas habituales en la materia. Específicamente, en referencia a la investigación, existen cuatro áreas complementarias de actuación, con diferente grado de granularidad: 1) Compartición y Administración de Recursos de Conocimiento, 2) Actividades y Unidades de Aprendizaje, 3) Programas de Desarrollo de Competencias, y 4) Redes para el Desarrollo de Competencias. Este artículo presenta los principales problemas por resolver para el desarrollo contínuo de competencias y describe las líneas de investigación definidas en el proyecto TENCompetence para abordarlos, incluyendo las principales técnicas en uso o de aplicación inmediata.This work has been sponsored by the EU project TENCompetence [www.tencompetence.org

Metamodeling approach to preference management in the semantic Web

2008 AAAI Workshop; Chicago, IL; United States; 13 July 2008 through 14 July 2008Preference is a superiority state to determine the preferable or the superior of one entity, property or constraint to another from a specified selection set. Preference issue is heavily studied in Semantic Web research area. The existing preference management approaches only consider the importance of concepts for capturing users' interests. This paper presents a metamodeling approach to preference management. Preference meta model consists of concepts and semantic relations to represent users' interests. Users may have the same type preferences in different domains. Thus, metamodeling must be used to define similar preferences for interoperability in different domains. In this paper, preference meta model defines a general storage structure to manage different types of preferences for personalized applications. Copyright © 2008, Association for the Advancement of Artificial Intelligence

An extension of ontology based databases to handle preferences

1th International Conference on Enterprise Information Systems; Milan; Italy; 6 May 2009 through 10 May 2009Ontologies have been defined to make explicit the semantics of data. With the emergence of the SemanticWeb, the amount of ontological data (or instances) available has increased. To manage such data, Ontology Based DataBases (OBDBs), that store ontologies and their instance data in the same repository have been proposed. These databases are associated with exploitation languages supporting description, querying, etc. on both ontologies and data. However, usually queries return a big amount of data that may be sorted in order to find the relevant ones. Moreover, in the current, few approaches considering user preferences when querying have been developed. Yet this problem is fundamental for many applications especially in the e-commerce domain. In this paper, we first propose an extension of an existing OBDB, called OntoDB through extension of their ontology model in order to support semantic description of preferences. Secondly, an extension of an ontology based query language, called OntoQL defined on OntoDB for querying ontological data with preferences is presented. Finally, an implementation of the proposed extensions are described

Sparq2l:towards support for subgraph extraction queries in rdf databases

Many applications in analytical domains often have the need to “connect the dots ” i.e., query about the structure of data. In bioinformatics for example, it is typical to want to query about interactions between proteins. The aim of such queries is to “extract ” relationships between entities i.e. paths from a data graph. Often, such queries will specify certain constraints that qualifying results must satisfy e.g. paths involving a set of mandatory nodes. Unfortunately, most present day Semantic Web query languages including the current draft of the anticipated recommendation SPARQL, lack the ability to express queries about arbitrary path structures in data. In addition, many systems that support some limited form of path queries rely on main memory graph algorithms limiting their applicability to very large scale graphs. In this paper, we present an approach for supporting Path Extraction queries. Our proposal comprises (i) a query language SPARQ2L which extends SPARQL with path variables and path variable constraint expressions, and (ii) a novel query evaluation framework based on efficient algebraic techniques for solving path problems which allows for path queries to be efficiently evaluated on disk resident RDF graphs. The effectiveness of our proposal is demonstrated by a performance evaluation of our approach on both real world and synthetic datasets

Query Answering in Ontologies under Preference Rankings

We present an ontological framework, based on preference rankings, that allows users to express their preferences between the knowledge explicitly available in the ontology. Using this formalism, the answers for a given query to an ontology can be ranked by preference, allowing users to retrieve the most preferred answers only. We provide a host of complexity results for the main computational tasks in this framework, for the general case, and for EL and DL-Litecore as underlying ontology languages

Uma ontologia para expressão de preferências de utilizadores em sistemas OLAP

Dissertação de mestrado em Informática (área de especialização em Sistemas de Suporte à Decisão)Na maioria dos casos em que se submete uma query a um sistema, recebemos uma “enxurrada” de dados como resposta, que em grande parte não nos interessa. Na realidade, ao se obter um qualquer record set, fazemos frequentemente uma forte triagem para que fiquemos apenas com aquilo que realmente nos interessa no âmbito do problema que temos em mãos. De facto, era muito útil que, de algum modo, fosse possível expressar as nossas preferências em termos daquilo que queremos obter aquando do lançamento de uma dada query. Diversas pesquisas têm sido feitas com o objectivo de conseguir expressar as preferências dos utilizadores, no sentido de apresentar resultados mais satisfatórios nas suas consultas, mais próximos daquilo que o utilizador pretende. As preferências são utilizadas como filtragem nas consultas efectuadas, com o objetivo de reduzir o volume de dados que potencialmente poderá ser apresentado ao utilizador. Estas ajudam, também, na criação de políticas com base nos perfis dos utilizadores. Assim, neste trabalho de dissertação é proposto estudar a possibilidade de melhorar a interação dos utilizadores com os sistemas OLAP, recorrendo para isso à Web Semântica, nomeadamente à utilização de um modelo ontológico e a técnicas mais avançadas de expressão de preferências.In most cases in which a query is submitted to a system, the response is a “flood” of data which largely does not have any interest to us. Actually, to obtain any record set, we often do a strong triage to obtain what really interests us in the scope of the problem that we currently have. In fact, it would be very helpful that, somehow, we had the possibility to express our preferences in terms of what we want to get at the launch of a given query. Several researches have been done with the purpose of getting the user preferences expressed, in order to provide a more satisfactory result of the queries, closer to what the user pretends. User preferences are used as a filter in the result set, with the aim of reducing the data volume that could, potentially, be presented to the user. They help, also, in creating policies based on user profiles. Thus, in this dissertation work, it is proposed to study the possibility of improving the interaction of users with OLAP systems, resorting to Semantic Web, in particular the use of an ontological model and the most advanced techniques of expression of preferences

Querying the semantic web with preferences

Abstract. Ranking is an important concept to avoid empty or overfull and unordered result sets. However, such scoring can only express total orders, which restricts its usefulness when several factors influence result relevance. A more flexible way to express relevance is the notion of preferences. Users state which kind of answers they ‘prefer ’ by adding soft constraints to their queries. Current approaches in the Semantic Web offer only limited facilities for specification of scoring and result ordering. There is no common language element to express and formalize ranking and preferences. We present a comprehensive extension of SPARQL which directly supports the expression of preferences. This includes formal syntax and semantics of preference expressions for SPARQL. Additionally, we report our implementation of preference query processing, which is based on the ARQ query engine