5,367 research outputs found

    A community based approach for managing ontology alignments

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    The Semantic Web is rapidly becoming a defacto distributed repository for semantically represented data, thus leveraging on the added on value of the network effect. Various ontology mapping techniques and tools have been devised to facilitate the bridging and integration of distributed data repositories. Nevertheless, ontology mapping can benefitfrom human supervision to increase accuracy of results. The spread of Web 2.0 approaches demonstrate the possibility of using collaborative techniques for reaching consensus. While a number of prototypes for collaborative ontology construction are being developed, collaborative ontology mapping is not yet well investigated. In this paper, we describe a prototype that combines off-the-shelf ontology mapping tools with social software techniques to enable users to collaborate on mapping ontologies

    Dynamic Change Evaluation for Ontology Evolution in the Semantic Web

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    Changes in an ontology may have a disruptive impact on any system using it. This impact may depend on structural changes such as introduction or removal of concept definitions, or it may be related to a change in the expected performance of the reasoning tasks. As the number of systems using ontologies is expected to increase, and given the open nature of the Semantic Web, introduction of new ontologies and modifications to existing ones are to be expected. Dynamically handling such changes, without requiring human intervention, becomes crucial. This paper presents a framework that isolates groups of related axioms in an OWL ontology, so that a change in one or more axioms can be automatically localised to a part of the ontology

    Is a Semantic Web Agent a Knowledge-Savvy Agent?

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    The issue of knowledge sharing has permeated the field of distributed AI and in particular, its successor, multiagent systems. Through the years, many research and engineering efforts have tackled the problem of encoding and sharing knowledge without the need for a single, centralized knowledge base. However, the emergence of modern computing paradigms such as distributed, open systems have highlighted the importance of sharing distributed and heterogeneous knowledge at a larger scale—possibly at the scale of the Internet. The very characteristics that define the Semantic Web—that is, dynamic, distributed, incomplete, and uncertain knowledge—suggest the need for autonomy in distributed software systems. Semantic Web research promises more than mere management of ontologies and data through the definition of machine-understandable languages. The openness and decentralization introduced by multiagent systems and service-oriented architectures give rise to new knowledge management models, for which we can’t make a priori assumptions about the type of interaction an agent or a service may be engaged in, and likewise about the message protocols and vocabulary used. We therefore discuss the problem of knowledge management for open multi-agent systems, and highlight a number of challenges relating to the exchange and evolution of knowledge in open environments, which pertinent to both the Semantic Web and Multi Agent System communities alike

    Towards a component-based framework for developing Semantic Web applications

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    For those outside the research community, to develop Semantic Web applications entails real difficulty. This difficulty is due in part to the lack of usable approaches for planning Semantic Web solutions, even though Semantic Web tools have already reached industrial maturity. We propose here the Semantic Web Framework, a component-based framework for analysing rapidly the required components, the dependencies between them, and selecting existing solutions. This approach has been tested with a number of industrial partners, which justifies the effort made in this direction

    Towards engineering ontologies for cognitive profiling of agents on the semantic web

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    Research shows that most agent-based collaborations suffer from lack of flexibility. This is due to the fact that most agent-based applications assume pre-defined knowledge of agents’ capabilities and/or neglect basic cognitive and interactional requirements in multi-agent collaboration. The highlight of this paper is that it brings cognitive models (inspired from cognitive sciences and HCI) proposing architectural and knowledge-based requirements for agents to structure ontological models for cognitive profiling in order to increase cognitive awareness between themselves, which in turn promotes flexibility, reusability and predictability of agent behavior; thus contributing towards minimizing cognitive overload incurred on humans. The semantic web is used as an action mediating space, where shared knowledge base in the form of ontological models provides affordances for improving cognitive awareness

    Community description

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    petersanders2015aThis document constitutes the 3rd revision of Ready4SmartCities’ Community Description onthe plan on how to build a community for the Ready4SmartCities roadmap, vision andoutcome, also in the light of the targeted data interoperability proposals work packages 2, 3and 4 dealt with. It intends to depict the project’s community of communities at the end ofthe project’s lifetime, the different means the project used to get in touch with it and the viewof building a “data community” via semantic web technologies. It recapitulates and criticallyassesses the problems encountered during the execution of the project concerninginteractions and a channel used, and discusses issues arising in the work to fully reach thetargeted audience(s)

    BIOLOGICAL TAXONOMY AND ONTOLOGY DEVELOPMENT: SCOPE AND LIMITATIONS

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    The prospects of integrating full-blown biological taxonomies into an ontological reasoning framework are critically reviewed. The common usage of a static 'snapshot' hierarchy in ontological representations of taxonomy is contrasted with a more realistic situation that involves dynamic, piece-meal revisions of particular taxonomic groups and requires alignment with relevant preceding perspectives. Taxonomic practice is characterized by a range of phenomena that are orthogonal to the logical background from which ontological entities and relationships originate, and therefore pose special challenges to ontological representation and reasoning. Among these phenomena are: (1) the notion that there is a single phylogenetic hierarchy in nature which taxonomy can only gradually approximate; (2) the evolvability of taxa which means that taxon-defining features may be lost in subordinate members or independently gained across multiple sections of the tree of life; (3) the hybrid approach of defining taxa both in reference to properties (intensional) and members (ostensive) which undermines the individual/class dichotomy sustaining conventional ontologies; (4) the idiosyncratic yet inferentially valuable usage of Linnaean ranks; (5) the indelible and semantically complex 250-year legacy of nomenclatural and taxonomic changes that characterizes the current system; (6) the insufficient taxonomic exploration of large portions of the tree of life; and the need to use a sophisticated terminology for aligning taxonomic entities in order to integrate both (7) single and (8) multiple hierarchies. It is suggested that research along the taxonomy/ontology interface should focus on either strictly nomenclatural entities or specialize in ontology-driven methods for producing alignments between multiple taxonomies

    Evaluating the combination of relaxation and argumentation in ontology matching negotiation

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    No decorrer dos últimos anos, os agentes (inteligentes) de software foram empregues como um método para colmatar as dificuldades associadas com a gestão, partilha e reutilização de um crescente volume de informação, enquanto as ontologias foram utilizadas para modelar essa mesma informação num formato semanticamente explícito e rico. À medida que a popularidade da Web Semântica aumenta e cada vez informação é partilhada sob a forma de ontologias, o problema de integração desta informação amplifica-se. Em semelhante contexto, não é expectável que dois agentes que pretendam cooperar utilizem a mesma ontologia para descrever a sua conceptualização do mundo. Inclusive pode revelar-se necessário que agentes interajam sem terem conhecimento prévio das ontologias utilizadas pelos restantes, sendo necessário que as conciliem em tempo de execução num processo comummente designado por Mapeamento de Ontologias [1]. O processo de mapeamento de ontologias é normalmente oferecido como um serviço aos agentes de negócio, podendo ser requisitado sempre que seja necessário produzir um alinhamento. No entanto, tendo em conta que cada agente tem as suas próprias necessidades e objetivos, assim como a própria natureza subjetiva das ontologias que utilizam, é possível que tenham diferentes interesses relativamente ao processo de alinhamento e que, inclusive, recorram aos serviços de mapeamento que considerem mais convenientes [1]. Diferentes matchers podem produzir resultados distintos e até mesmo contraditórios, criando-se assim conflitos entre os agentes. É necessário que se proceda então a uma tentativa de resolução dos conflitos existentes através de um processo de negociação, de tal forma que os agentes possam chegar a um consenso relativamente às correspondências que devem ser utilizadas na tradução de mensagens a trocar. A resolução de conflitos é considerada uma métrica de grande importância no que diz respeito ao processo de negociação [2]: considera-se que existe uma maior confiança associada a um alinhamento quanto menor o número de conflitos por resolver no processo de negociação que o gerou. Desta forma, um alinhamento com um número elevado de conflitos por resolver apresenta uma confiança menor que o mesmo alinhamento associado a um número elevado de conflitos resolvidos. O processo de negociação para que dois ou mais agentes gerem e concordem com um alinhamento é denominado de Negociação de Mapeamentos de Ontologias. À data existem duas abordagens propostas na literatura: (i) baseadas em Argumentação (e.g. [3] [4]) e (ii) baseadas em Relaxamento [5] [6]. Cada uma das propostas expostas apresenta um número de vantagens e limitações. Foram propostas várias formas de combinação das duas técnicas [2], com o objetivo de beneficiar das vantagens oferecidas e colmatar as suas limitações. No entanto, à data, não são conhecidas experiências documentadas que possam provar tal afirmação e, como tal, não é possível atestar que tais combinações tragam, de facto, o benefício que pretendem. O trabalho aqui apresentado pretende providenciar tais experiências e verificar se a afirmação de melhorias em relação aos resultados das técnicas individuais se mantém. Com o objetivo de permitir a combinação e de colmatar as falhas identificadas, foi proposta uma nova abordagem baseada em Relaxamento, que é posteriormente combinada com as abordagens baseadas em Argumentação. Os seus resultados, juntamente com os da combinação, são aqui apresentados e discutidos, sendo possível identificar diferenças nos resultados gerados por combinações diferentes e possíveis contextos de utilização.Agent-based Ontology Alignment Negotiation process aims to generate an alignment through the interaction of two or more agents. When these agents exploit different matching services they can reach incompatible alignments, giving rise to conflicts. In such cases it is necessary that they engage in a negotiation process in order to achieve consensus. Two different types of ontology matching negotiation approaches can be found in literature: (i) Relaxation-based and (ii) Argumentation-based. Each of these approaches has its advantages and limitations. To benefit from both techniques’ advantages and overcome their limitations, several ways of combining them have been proposed. To the best of our knowledge however, no experiments have been described and no results regarding these combinations have been reported in literature. This work aims to provide such results by implementing and comparing different combinations of Relaxation-based and Argumentation-based approaches. After carefully analyzing these approaches, we concluded that the state of the art Relaxation-based approach needed improvement before it could be combined with Argumentation-based approaches. In this context, a new proposal for the Relaxation-based approach is described and a thorough analysis of the results achieved through two of the proposed combinations. The presented results allow identifying the different benefits of each combination, thus making it possible for developers to choose which one fits their requirements for the generated alignment
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