895 resultados para Semantic web


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Pós-graduação em Ciência da Informação - FFC

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Pós-graduação em Ciência da Informação - FFC

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Pós-graduação em Ciência da Computação - IBILCE

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Pós-graduação em Ciência da Computação - IBILCE

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Introduction: In the Web environment, there is a need for greater care with regard to the processing of descriptive and thematic information. The concern with the recovery of information in computer systems precedes the development of the first personal computers. Models of information retrieval have been and are today widely used in databases specific to a field whose scope is known. Objectives: Verify how the issue of relevance is treated in the main computer models of information retrieval and, especially, as the issue is addressed in the future of the Web, the called Semantic Web. Methodology: Bibliographical research. Results: In the classical models studied here, it was realized that the main concern is retrieving documents whose description is closest to the search expression used by the user, which does not necessarily imply that this really needs. In semantic retrieval is the use of ontologies, feature that extends the user's search for a wider range of possible relevant options. Conclusions: The relevance is a subjective judgment and inherent to the user, it will depend on the interaction with the system and especially the fact that he expects to recover in your search. Systems that are based on a model of relevance are not popular, because it requires greater interaction and depend on the user's disposal. The Semantic Web is so far the initiative more efficient in the case of information retrieval in the digital environment.

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The traditional characteristics and challenges for organizing and searching information on the World Wide Web are outlined and reviewed. The classification features of two of these methods, such as Google, in the case of automated search engines, and Yahoo! Directory, in the case of subject directories are analyzed. Recent advances in the Semantic Web, particularly the growing application of ontologies and Linked Data are also reviewed. Finally, some problems and prospects related to the use of classification and indexing on the World Wide Web are discussed, emphasizing the need of rethinking the role of classification in the organization of these resources and outlining the possibilities of applying Ranganathan's facet theories of classification.

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Two of the main features of today complex software systems like pervasive computing systems and Internet-based applications are distribution and openness. Distribution revolves around three orthogonal dimensions: (i) distribution of control|systems are characterised by several independent computational entities and devices, each representing an autonomous and proactive locus of control; (ii) spatial distribution|entities and devices are physically distributed and connected in a global (such as the Internet) or local network; and (iii) temporal distribution|interacting system components come and go over time, and are not required to be available for interaction at the same time. Openness deals with the heterogeneity and dynamism of system components: complex computational systems are open to the integration of diverse components, heterogeneous in terms of architecture and technology, and are dynamic since they allow components to be updated, added, or removed while the system is running. The engineering of open and distributed computational systems mandates for the adoption of a software infrastructure whose underlying model and technology could provide the required level of uncoupling among system components. This is the main motivation behind current research trends in the area of coordination middleware to exploit tuple-based coordination models in the engineering of complex software systems, since they intrinsically provide coordinated components with communication uncoupling and further details in the references therein. An additional daunting challenge for tuple-based models comes from knowledge-intensive application scenarios, namely, scenarios where most of the activities are based on knowledge in some form|and where knowledge becomes the prominent means by which systems get coordinated. Handling knowledge in tuple-based systems induces problems in terms of syntax - e.g., two tuples containing the same data may not match due to differences in the tuple structure - and (mostly) of semantics|e.g., two tuples representing the same information may not match based on a dierent syntax adopted. Till now, the problem has been faced by exploiting tuple-based coordination within a middleware for knowledge intensive environments: e.g., experiments with tuple-based coordination within a Semantic Web middleware (surveys analogous approaches). However, they appear to be designed to tackle the design of coordination for specic application contexts like Semantic Web and Semantic Web Services, and they result in a rather involved extension of the tuple space model. The main goal of this thesis was to conceive a more general approach to semantic coordination. In particular, it was developed the model and technology of semantic tuple centres. It is adopted the tuple centre model as main coordination abstraction to manage system interactions. A tuple centre can be seen as a programmable tuple space, i.e. an extension of a Linda tuple space, where the behaviour of the tuple space can be programmed so as to react to interaction events. By encapsulating coordination laws within coordination media, tuple centres promote coordination uncoupling among coordinated components. Then, the tuple centre model was semantically enriched: a main design choice in this work was to try not to completely redesign the existing syntactic tuple space model, but rather provide a smooth extension that { although supporting semantic reasoning { keep the simplicity of tuple and tuple matching as easier as possible. By encapsulating the semantic representation of the domain of discourse within coordination media, semantic tuple centres promote semantic uncoupling among coordinated components. The main contributions of the thesis are: (i) the design of the semantic tuple centre model; (ii) the implementation and evaluation of the model based on an existent coordination infrastructure; (iii) a view of the application scenarios in which semantic tuple centres seem to be suitable as coordination media.

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Questa tesi progettuale nasce per integrare gli sforzi attuali sullo sviluppo del web semantico. La piattaforma di riferimento sulla quale è stato svolto il presente lavoro è SMART-M3. Questa piattaforma mette a disposizione uno spazio condiviso di informazioni, rappresentate e accessibili secondo le tecnologie del web semantico. In questo scenario, nasce la necessità di disporre di un'interfaccia web capace di interagire con la piattaforma - in grado di risolvere la complessità intrinseca dei dati semantici - allo scopo di averne un completo controllo; ricerche precedenti a questo proposito hanno dato come frutto una libreria PHP che mi è stata consegnata come strumento per lo sviluppo dell'interfaccia. La tesi si è articolata in 3 fasi principali: una fase iniziale di documentazione sull'argomento, eseguita principalmente sul libro “A developer's guide to the semantic web” di Liyang Yu e sulla tesi “Ontologie per il web semantico: un'analisi comparativa.” di Indrit Beqiri; una seconda fase, quella principale, di sviluppo del progetto informatico; una terza fase, infine, di sviluppo di questo elaborato di tesi, da considerarsi come la trattazione di tutto il percorso soprascritto, dall'inizio alla fine, secondo l'ordine cronologico in cui si svolto l'intero processo della tesi.

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Con questa dissertazione di tesi miro ad illustrare i risultati della mia ricerca nel campo del Semantic Publishing, consistenti nello sviluppo di un insieme di metodologie, strumenti e prototipi, uniti allo studio di un caso d‟uso concreto, finalizzati all‟applicazione ed alla focalizzazione di Lenti Semantiche (Semantic Lenses).

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Obiettivo della tesi è analizzare e testare i principali approcci di Machine Learning applicabili in contesti semantici, partendo da algoritmi di Statistical Relational Learning, quali Relational Probability Trees, Relational Bayesian Classifiers e Relational Dependency Networks, per poi passare ad approcci basati su fattorizzazione tensori, in particolare CANDECOMP/PARAFAC, Tucker e RESCAL.

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Lo scopo del progetto Bird-A è di mettere a disposizione uno strumento basato su ontologie per progettare un'interfaccia web collaborativa di creazione, visualizzazione, modifica e cancellazione di dati RDF e di fornirne una prima implementazione funzionante. La visione che sta muovendo la comunità del web semantico negli ultimi anni è quella di creare un Web basato su dati strutturati tra loro collegati, più che su documenti. Questo modello di architettura prende il nome di Linked Data ed è basata sulla possibilità di considerare cose, concetti, persone come risorse identificabili tramite URI e di poter fornire informazioni e descrivere collegamenti tra queste risorse attraverso l'uso di formati standard come RDF. Ciò che ha però frenato la diffusione di questi dati strutturati ed interconnessi sono stati gli alti requisiti di competenze tecniche necessarie sia alla loro creazione che alla loro fruizione. Il progetto Bird-A si prefigge di semplificare la creazione e la fruizione di dati RDF, favorendone la condivisione e la diffusione anche fra persone non dotate di conoscenze tecniche specifiche.

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Continuous advancements in technology have led to increasingly comprehensive and distributed product development processes while in pursuit of improved products at reduced costs. Information associated with these products is ever changing, and structured frameworks have become integral to managing such fluid information. Ontologies and the Semantic Web have emerged as key alternatives for capturing product knowledge in both a human-readable and computable manner. The primary and conclusive focus of this research is to characterize relationships formed within methodically developed distributed design knowledge frameworks to ultimately provide a pervasive real-time awareness in distributed design processes. Utilizing formal logics in the form of the Semantic Web’s OWL and SWRL, causal relationships are expressed to guide and facilitate knowledge acquisition as well as identify contradictions between knowledge in a knowledge base. To improve the efficiency during both the development and operational phases of these “intelligent” frameworks, a semantic relatedness algorithm is designed specifically to identify and rank underlying relationships within product development processes. After reviewing several semantic relatedness measures, three techniques, including a novel meronomic technique, are combined to create AIERO, the Algorithm for Identifying Engineering Relationships in Ontologies. In determining its applicability and accuracy, AIERO was applied to three separate, independently developed ontologies. The results indicate AIERO is capable of consistently returning relatedness values one would intuitively expect. To assess the effectiveness of AIERO in exposing underlying causal relationships across product development platforms, a case study involving the development of an industry-inspired printed circuit board (PCB) is presented. After instantiating the PCB knowledge base and developing an initial set of rules, FIDOE, the Framework for Intelligent Distributed Ontologies in Engineering, was employed to identify additional causal relationships through extensional relatedness measurements. In a conclusive PCB redesign, the resulting “intelligent” framework demonstrates its ability to pass values between instances, identify inconsistencies amongst instantiated knowledge, and identify conflicting values within product development frameworks. The results highlight how the introduced semantic methods can enhance the current knowledge acquisition, knowledge management, and knowledge validation capabilities of traditional knowledge bases.

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Internet of Things based systems are anticipated to gain widespread use in industrial applications. Standardization efforts, like 6L0WPAN and the Constrained Application Protocol (CoAP) have made the integration of wireless sensor nodes possible using Internet technology and web-like access to data (RESTful service access). While there are still some open issues, the interoperability problem in the lower layers can now be considered solved from an enterprise software vendors' point of view. One possible next step towards integration of real-world objects into enterprise systems and solving the corresponding interoperability problems at higher levels is to use semantic web technologies. We introduce an abstraction of real-world objects, called Semantic Physical Business Entities (SPBE), using Linked Data principles. We show that this abstraction nicely fits into enterprise systems, as SPBEs allow a business object centric view on real-world objects, instead of a pure device centric view. The interdependencies between how currently services in an enterprise system are used and how this can be done in a semantic real-world aware enterprise system are outlined, arguing for the need of semantic services and semantic knowledge repositories. We introduce a lightweight query language, which we use to perform a quantitative analysis of our approach to demonstrate its feasibility.

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A social Semantic Web empowers its users to have access to collective Web knowledge in a simple manner, and for that reason, controlling online privacy and reputation becomes increasingly important, and must be taken seriously. This chapter presents Fuzzy Cognitive Maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. With this in mind, a conceptual framework for Web knowledge aggregation, representation, and reasoning is introduced along with a use case, in which the importance of investigative searching for online privacy and reputation is highlighted. Thereby it is demonstrated how a user can establish a positive online presence.