254 research outputs found

    Expression de requĂȘtes SPARQL Ă  partir de patrons: prise en compte des relations

    Get PDF
    International audienceNotre objectif est de masquer la difficultĂ© d'exprimer une requĂȘte dans le langage de graphes SPARQL. Nous proposons un mĂ©canisme permettant d'exprimer des requĂȘtes dans un langage pivot trĂšs simple, constituĂ© essentiellement de mots-clĂ©s et de relations entre ces mots-clĂ©s. Notre systĂšme associe les mots-clĂ©s et les Ă©lĂ©ments de l'ontologie (concepts, relations, instances) correspondants. Il sĂ©lectionne alors des patrons de requĂȘtes prĂ©-Ă©crits, puis les instancie Ă  partir des mots-clĂ©s de la requĂȘte initiale. Plusieurs requĂȘtes sont alors prĂ©sentĂ©es Ă  l'utilisateur sous forme de phrases descriptives en langue naturelle. L'utilisateur sĂ©lectionne alors la requĂȘte qui l'intĂ©resse. La requĂȘte SPARQL est alors gĂ©nĂ©rĂ©e

    Architectural approaches to build the Museum of the Person

    Get PDF
    The Museum of the Person (Museu da Pessoa, MP) is a virtual museum aimed at exhibiting life stories of common people. Its assets are composed of several interviews involving people whose stories we want to perpetuate. So the museum holds an heterogeneous collection of XML (eXtensible Markup Language) documents that constitute the working repository. The main idea is to extract automatically the information included in the repository in order to build the web pages that realize the museum's exhibition rooms. This project started by creating a specific ontology (OntoMP) for the knowledge repository of MP. That ontology is intended to allow a conceptual navigation over the available information. We will adopt the standard for museum ontologies CIDOC-CRM (CIDOC Conceptual Reference Model) refined with FOAF to represent OntoMP. The objective of this paper is to discuss different architectural approaches to build a system that will create the virtual rooms from the XML repository to enable visitors to lookup individual life stories and also intercross information among them. The first architecture is based on a TripleStore and uses SPARQL (SPARQL Protocol and RDF Query Language) technology to extract the information, while the second proposal is based on a Relational Database and uses CaVa Generator to query the repository and build the exhibition spaces.info:eu-repo/semantics/publishedVersio

    Solving problems of data heterogeneity, semantic heterogeneity and data inequality : an approach using ontologies

    Get PDF
    Knowledge is people’s personal map and people’s personal model of the world. Knowledge acquisition involves complex cognitive processes such as perception, communication, and reasoning. According to the knowledge differences, then it is possible for people have a different perception to attain awareness or understand the environment or reality. This paper provides a case study where there is a group of people in different communities managing data using different perceptions, different concepts, different terms (terminologies), and different semantics to represent the same reality. Perceptions are converted into data, and then saved into separate storage devices that are not connected to each other. Each user – belonging to different communities - use different terminologies in collecting data and as a consequence they also get different results of that exercise. It is not a problem if the different results are used for each community, the problem occur if people need to take data from another communities, sharing, collaborating and using it to get a bigger solution. In this paper we present an approach to generate a common set of terms based on the terms of several and different storage devices, used by different communities, in order to make data retrieval independent of the different perceptions and terminologies used by those communities. We use ontologies to represent the knowledge and discuss the use of mapping and integration techniques to find correspondences between the concepts used in those ontologies. We discuss too how to derive a common ontology to be used by all the communities. We can find in literature several documents about the theoretical concepts and techniques that can be used to solve the described problem. However, in this paper we are presenting a real implementation of a system using those concepts

    PRIVAFRAME: A Frame-Based Knowledge Graph for Sensitive Personal Data

    Get PDF
    The pervasiveness of dialogue systems and virtual conversation applications raises an important theme: the potential of sharing sensitive information, and the consequent need for protection. To guarantee the subject’s right to privacy, and avoid the leakage of private content, it is important to treat sensitive information. However, any treatment requires firstly to identify sensitive text, and appropriate techniques to do it automatically. The Sensitive Information Detection (SID) task has been explored in the literature in different domains and languages, but there is no common benchmark. Current approaches are mostly based on artificial neural networks (ANN) or transformers based on them. Our research focuses on identifying categories of personal data in informal English sentences, by adopting a new logical-symbolic approach, and eventually hybridising it with ANN models. We present a frame-based knowledge graph built for personal data categories defined in the Data Privacy Vocabulary (DPV). The knowledge graph is designed through the logical composition of already existing frames, and has been evaluated as background knowledge for a SID system against a labeled sensitive information dataset. The accuracy of PRIVAFRAME reached 78%. By comparison, a transformer-based model achieved 12% lower performance on the same dataset. The top-down logical-symbolic frame-based model allows a granular analysis, and does not require a training dataset. These advantages lead us to use it as a layer in a hybrid model, where the logical SID is combined with an ANNs SID tested in a previous study by the authors
    • 

    corecore