971 resultados para Linked Data


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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Il progetto QRPlaces - Semantic Events, oggetto di questo lavoro, focalizza l’attenzione sull’analisi, la progettazione e l’implementazione di un sistema che sia in grado di modellare i dati, relativi a diversi eventi facenti parte del patrimonio turistico - culturale della Regione Emilia Romagna 1, rendendo evidenti i vantaggi associati ad una rappresentazione formale incentrata sulla Semantica. I dati turistico - culturali sono intesi in questo ambito sia come una rappresentazione di “qualcosa che accade in un certo punto ad un certo momento” (come ad esempio un concerto, una sagra, una raccolta fondi, una rappresentazione teatrale e quant’altro) sia come tradizioni e costumi che costituiscono il patrimonio turistico-culturale e a cui si fa spesso riferimento con il nome di “Cultural Heritage”. Essi hanno la caratteristica intrinseca di richiedere una conoscenza completa di diverse informa- zioni correlata, come informazioni di geo localizzazione relative al luogo fisico che ospita l’evento, dati biografici riferiti all’autore o al soggetto che è presente nell’evento piuttosto che riferirsi ad informazioni che descrivono nel dettaglio tutti gli oggetti, come teatri, cinema, compagnie teatrali che caratterizzano l’evento stesso. Una corretta rappresentazione della conoscenza ad essi legata richiede, pertanto, una modellazione in cui i dati possano essere interconnessi, rivelando un valore informativo che altrimenti resterebbe nascosto. Il lavoro svolto ha avuto lo scopo di realizzare un dataset rispondente alle caratteristiche tipiche del Semantic Web grazie al quale è stato possibile potenziare il circuito di comunicazione e informazione turistica QRPlaces 2. Nello specifico, attraverso la conversione ontologica di dati di vario genere relativi ad eventi dislocati nel territorio, e sfruttando i principi e le tecnologie del Linked Data, si è cercato di ottenere un modello informativo quanto più possibile correlato e arricchito da dati esterni. L’obiettivo finale è stato quello di ottenere una sorgente informativa di dati interconnessi non solo tra loro ma anche con quelli presenti in sorgenti esterne, dando vita ad un percorso di collegamenti in grado di evidenziare una ricchezza informativa utilizzabile per la creazione di valore aggiunto che altrimenti non sarebbe possibile ottenere. Questo aspetto è stato realizzato attraverso un’in- terfaccia di MashUp che utilizza come sorgente il dataset creato e tutti i collegamenti con la rete del Linked Data, in grado di reperire informazioni aggiuntive multi dominio.

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This thesis aims at investigating methods and software architectures for discovering what are the typical and frequently occurring structures used for organizing knowledge in the Web. We identify these structures as Knowledge Patterns (KPs). KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Then we present K~ore, a software architecture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization.

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Obiettivo di questa tesi dal titolo “Analisi di tecniche per l’estrazione di informazioni da documenti testuali e non strutturati” è quello di mostrare tecniche e metodologie informatiche che permettano di ricavare informazioni e conoscenza da dati in formato testuale. Gli argomenti trattati includono l'analisi di software per l'estrazione di informazioni, il web semantico, l'importanza dei dati e in particolare i Big Data, Open Data e Linked Data. Si parlerà inoltre di data mining e text mining.

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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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Genome-wide association studies (GWAS) have identified numerous single-nucleotide polymorphisms (SNPs) at four loci (SNCA, PARK16, LRRK2, BST1) that can modulate the risk of Parkinson's disease (PD). The strength of these associations has yet to be clarified in Mainland China. Ethnic specific effect is an important consideration in GWAS analysis. Using a case-control methodology, we genotyped multiple SNPs at these four loci to investigate their association with risk of PD in Mainland China. A total of 1,146 study subjects comprising 636 patients with PD and 510 unrelated healthy controls were recruited. The minor alleles at SNPs rs894278, rs1994090, rs2046932, rs4698412, and rs7304279 were found to be significantly higher in cases than in controls, while the minor alleles were found to significantly reduce the risk of developing PD at SNPs rs823128, rs823156, rs6532194, rs1191532, and rs16856139. These associations remained after taking into considerations the effects of age and gender. We showed that multiple SNPs at LRRK2 and SNCA increase risk of PD, while PARK16 SNPs are associated with a lower risk of PD in China. Our study findings will contribute to further research using GWAS-linked data and research on ethnic specific effect of common variants.

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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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Colorectal cancer (CRC) is the third leading cancer in both incidence and mortality in Texas. This study investigated the adherence of CRC treatment to standard treatment guidelines and the association between standard treatment and CRC survival in Texas. The author used Texas Cancer Registry (TCR) and Medicare linked data to study the CRC treatment patterns and factors associated with standard treatment in patients who were more than 65 years old and were diagnosed in 2001 through 2007. We also determined whether adherence to standard treatment affect patients' survival. Multiple logistic regression and Cox regression analysis were used to analyze our data. Both regression models are adjusted for demographic characteristics and tumor characteristics. We found that for the 3977 regional colon cancer patients 80 years old or younger, 60.2% of them received chemotherapy, in adherence to the recommended treatment guidelines. People with younger age, female gender, higher education and lower comorbidity score are more likely adherent to this surgery guideline. Patients' adherence to chemotherapy in this cohort have better survival compared to those who are not (HR: 0.76, 95% CI: 0.68-0.84). For the 12709 colon cancer patients treated with surgery, 49.3% have more than 12 lymph nodes removed, in adherence to the treatment guidelines. People with younger age, female gender, higher education, regional stage, lager tumor size and lower comorbidity score are more likely to adherent to this surgery guideline. Patients with more than 12 lymph nodes removed in this cohort have better survival (HR: 0.86, 95% CI: 0.82-0.91). For the 1211 regional rectal cancer patients 80 years old or younger, 63.2% of them were adherent to radiation treatment. People with smaller tumor size and lower comorbidity score are more likely to adherent to this radiation guideline. There is no significant survival difference between radiation adherent patients and non-adherent patients (HR: 1.03, 95% CI: 0.82-1.29). For the 1122 regional rectal cancer patients 80 years old or younger who were treated with surgery, 76.0% of them received postoperative chemotherapy, in adherence to the treatment guidelines. People with younger age and smaller comorbidity score are related with higher adherence rate. Patients adherent with adjuvant chemotherapy in this cohort have better survival than those were not adherent (HR: 0.60, 95% CI: 0.45-0.79).^

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Enriching knowledge bases with multimedia information makes it possible to complement textual descriptions with visual and audio information. Such complementary information can help users to understand the meaning of assertions, and in general improve the user experience with the knowledge base. In this paper we address the problem of how to enrich ontology instances with candidate images retrieved from existing Web search engines. DBpedia has evolved into a major hub in the Linked Data cloud, interconnecting millions of entities organized under a consistent ontology. Our approach taps into the Wikipedia corpus to gather context information for DBpedia instances and takes advantage of image tagging information when this is available to calculate semantic relatedness between instances and candidate images. We performed experiments with focus on the particularly challenging problem of highly ambiguous names. Both methods presented in this work outperformed the baseline. Our best method leveraged context words from Wikipedia, tags from Flickr and type information from DBpedia to achieve an average precision of 80%.

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In the beginning of the 90s, ontology development was similar to an art: ontology developers did not have clear guidelines on how to build ontologies but only some design criteria to be followed. Work on principles, methods and methodologies, together with supporting technologies and languages, made ontology development become an engineering discipline, the so-called Ontology Engineering. Ontology Engineering refers to the set of activities that concern the ontology development process and the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. Thanks to the work done in the Ontology Engineering field, the development of ontologies within and between teams has increased and improved, as well as the possibility of reusing ontologies in other developments and in final applications. Currently, ontologies are widely used in (a) Knowledge Engineering, Artificial Intelligence and Computer Science, (b) applications related to knowledge management, natural language processing, e-commerce, intelligent information integration, information retrieval, database design and integration, bio-informatics, education, and (c) the Semantic Web, the Semantic Grid, and the Linked Data initiative. In this paper, we provide an overview of Ontology Engineering, mentioning the most outstanding and used methodologies, languages, and tools for building ontologies. In addition, we include some words on how all these elements can be used in the Linked Data initiative.

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Recently, the Semantic Web has experienced significant advancements in standards and techniques, as well as in the amount of semantic information available online. Nevertheless, mechanisms are still needed to automatically reconcile information when it is expressed in different natural languages on the Web of Data, in order to improve the access to semantic information across language barriers. In this context several challenges arise [1], such as: (i) ontology translation/localization, (ii) cross-lingual ontology mappings, (iii) representation of multilingual lexical information, and (iv) cross-lingual access and querying of linked data. In the following we will focus on the second challenge, which is the necessity of establishing, representing and storing cross-lingual links among semantic information on the Web. In fact, in a “truly” multilingual Semantic Web, semantic data with lexical representations in one natural language would be mapped to equivalent or related information in other languages, thus making navigation across multilingual information possible for software agents.

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Lexica and terminology databases play a vital role in many NLP applications, but currently most such resources are published in application-specific formats, or with custom access interfaces, leading to the problem that much of this data is in ‘‘data silos’’ and hence difficult to access. The Semantic Web and in particular the Linked Data initiative provide effective solutions to this problem, as well as possibilities for data reuse by inter-lexicon linking, and incorporation of data categories by dereferencable URIs. The Semantic Web focuses on the use of ontologies to describe semantics on the Web, but currently there is no standard for providing complex lexical information for such ontologies and for describing the relationship between the lexicon and the ontology. We present our model, lemon, which aims to address these gaps

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Recently we have seen a large increase in the amount of geospatial data that is being published using RDF and Linked Data principles. Eorts such as the W3C Geo XG, and most recently the GeoSPARQL initiative are providing the necessary vocabularies to pub- lish this kind of information on the Web of Data. In this context it is necessary to develop applications that consume and take advantage of these geospatial datasets. In this paper we present map4rdf, a faceted browsing tool for exploring and visualizing RDF datasets enhanced with geospatial information.

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Because of the growing availability of third-party APIs, services, widgets and any other reusable web component, mashup developers now face a vast amount of candidate components for their developments. Moreover, these components quite often are scattered in many different repositories and web sites, which makes difficult their selection or discovery. In this paper, we discuss the problem of component selection in Service-Oriented Architectures (SOA) and Mashup-Driven Development, and introduce the Linked Mashups Ontology (LiMOn), a model that allows describing mashups and their components for integrating and sharing mashup information such as categorization or dependencies. The model has allowed the building of an integrated, centralized metadirectory of web components for query and selection, which has served to evaluate the model. The metadirectory allows accessing various heterogeneous repositories of mashups and web components while using external information from the Linked Data cloud, helping mashup development.