Дисертації з теми "RDF datasets"
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AZEVEDO, MARCELO COHEN DE. "AN APPLICATION BUILDER FOR QUERING RDF/RDFS DATASETS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=15978@1.
Повний текст джерелаCom o crescimento da web semântica, cada vez mais bases de dados em RDF contendo todo tipo de informações, nos mais variados domínios, estão disponíveis para acesso na Internet. Para auxiliar o acesso e a integração dessas informações, esse trabalho apresenta uma ferramenta que permite a geração de aplicações para consultas a bases em RDF e RDFS através da programação por exemplo. Usuários podem criar casos de uso através de operações simples em cima do modelo RFDS da própria base. Esses casos de uso podem ser generalizados e compartilhados com outros usuários, que podem reutilizá-los. Com esse compartilhamento, cria-se a possibilidade desses casos de uso serem customizados e evoluídos colaborativamente no próprio ambiente em que foram desenvolvidos. Novas operações também podem ser criadas e compartilhadas, o que contribui para o aumento gradativo do poder da ferramenta. Finalmente, utilizando um conjunto desses casos de uso, é possível gerar uma aplicação web que abstraia o modelo RDF em que os dados estão representados, tornando possível o acesso a essas informações por usuários que não conheçam o modelo RDF.
Due to increasing popularity of the semantic web, more data sets, containing information about varied domains, have become available for access in the Internet. This thesis proposes a tool to assist accessing and exploring this information. This tool allows the generation of applications for querying databases in RDF and RDFS through programming by example. Users are able to create use cases through simple operations using the RDFS model. These use cases can be generalized and shared with other users, who can reuse them. The shared use cases can be customized and extended collaboratively in the environment which they were developed. New operations can also be created and shared, making the tool increasingly more powerful. Finally, using a set of use cases, it’s possible to generate a web application that abstracts the RDF model where the data is represented, making it possible for lay users to access this information without any knowledge of the RDF model.
Arndt, Natanael, Norman Radtke, and Michael Martin. "Distributed collaboration on RDF datasets using Git." Universität Leipzig, 2016. https://ul.qucosa.de/id/qucosa%3A15781.
Повний текст джерелаFernández, Javier D., Miguel A. Martínez-Prieto, Axel Polleres, and Julian Reindorf. "HDTQ: Managing RDF Datasets in Compressed Space." Springer International Publishing, 2018. http://epub.wu.ac.at/6482/1/HDTQ.pdf.
Повний текст джерелаFernandez, Garcia Javier David, Sabrina Kirrane, Axel Polleres, and Simon Steyskal. "HDT crypt: Compression and Encryption of RDF Datasets." IOS Press, 2018. http://epub.wu.ac.at/6489/1/HDTCrypt%2DSWJ.pdf.
Повний текст джерелаSejdiu, Gezim [Verfasser]. "Efficient Distributed In-Memory Processing of RDF Datasets / Gezim Sejdiu." Bonn : Universitäts- und Landesbibliothek Bonn, 2020. http://d-nb.info/1221669214/34.
Повний текст джерелаMoreno, Vega José Ignacio. "A faceted browsing interface for diverse Large-Scale RDF Datasets." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168108.
Повний текст джерелаLas bases de conocimiento en RDF contienen información acerca de millones de recursos, las cuales son consultadas utilizando el lenguaje estándar de consultas para RDF: SPARQL. Sin embargo, esta información no está accesible fácilmente porque requiere conocer el lenguaje SPARQL y la estructura de los datos a consultar; requisitos que no cumple un usuario común de internet. Se propone una interfaz de navegación por facetas para estos datos de gran tamaño que no requiere conocimientos previos de la estructura ni de SPARQL. La navegación por facetas consiste en agregar filtros (conocidos como facetas) para mostrar únicamente los elementos que cumplen los requisitos. Interfaces de navegación por facetas para RDF existentes no escalan bien para las bases de conocimientos actuales. Se propone un nuevo sistema que crea índices para búsquedas fáciles y rápidas sobre los datos, permitiendo calcular y sugerir facetas al usuario. Para validar la escalabilidad y eficiencia del sistema, se escogió Wikidata como la base de datos de gran tamaño para realizar los experimentos de desempeño. Luego, se realizó un estudio de usuarios para evaluar la usabilidad e interacción del sistema, los resultados obtenidos muestran en qué aspectos el sistema desempeña bien y cuáles pueden ser mejorados. Un prototipo final junto a un cuestionario fue enviado a contribuidores de Wikidata para descubrir como este sistema puede ayudar a la comunidad.
Arndt, Natanael, and Norman Radtke. "Quit diff: calculating the delta between RDF datasets under version control." Universität Leipzig, 2016. https://ul.qucosa.de/id/qucosa%3A15780.
Повний текст джерелаSherif, Mohamed Ahmed Mohamed. "Automating Geospatial RDF Dataset Integration and Enrichment." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-215708.
Повний текст джерелаRihany, Mohamad. "Keyword Search and Summarization Approaches for RDF Dataset Exploration." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG030.
Повний текст джерелаAn increasing number of datasets are published on the Web, expressed in the standard languages proposed by the W3C such as RDF, RDF (S), and OWL. These datasets represent an unprecedented amount of data available for users and applications. In order to identify and use the relevant datasets, users and applications need to explore them using queries written in SPARQL, a query language proposed by the W3C. But in order to write a SPARQL query, a user should not only be familiar with the query language but also have knowledge about the content of the RDF dataset in terms of the resources, classes or properties it contains. The goal of this thesis is to provide approaches to support the exploration of these RDF datasets. We have studied two alternative and complementary exploration techniques, keyword search and summarization of an RDF dataset. Keyword search returns RDF graphs in response to a query expressed as a set of keywords, where each resulting graph is the aggregation of elements extracted from the source dataset. These graphs represent possible answers to the keyword query, and they can be ranked according to their relevance. Keyword search in RDF datasets raises the following issues: (i) identifying for each keyword in the query the matching elements in the considered dataset, taking into account the differences of terminology between the keywords and the terms used in the RDF dataset, (ii) combining the matching elements to build the result by defining aggregation algorithms that find the best way of linking matching elements, and finally (iii), finding appropriate metrics to rank the results, as several matching elements may exist for each keyword and consequently several graphs may be returned. In our work, we propose a keyword search approach that addresses these issues. Providing a summarized view of an RDF dataset can help a user in identifying if this dataset is relevant to his needs, and in highlighting its most relevant elements. This could be useful for the exploration of a given dataset. In our work, we propose a novel summarization approach based on the underlying themes of a dataset. Our theme-based summarization approach consists of extracting the existing themes in a data source, and building the summarized view so as to ensure that all these discovered themes are represented. This raises the following questions: (i) how to identify the underlying themes in an RDF dataset? (ii) what are the suitable criteria to identify the relevant elements in the themes extracted from the RDF graph? (iii) how to aggregate and connect the relevant elements to create a theme summary? and finally, (iv) how to create the summary for the whole RDF graph from the generated theme summaries? In our work, we propose a theme-based summarization approach for RDF datasets which answers these questions and provides a summarized representation ensuring that each theme is represented proportionally to its importance in the initial dataset
Ben, Ellefi Mohamed. "La recommandation des jeux de données basée sur le profilage pour le liage des données RDF." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT276/document.
Повний текст джерелаWith the emergence of the Web of Data, most notably Linked Open Data (LOD), an abundance of data has become available on the web. However, LOD datasets and their inherent subgraphs vary heavily with respect to their size, topic and domain coverage, the schemas and their data dynamicity (respectively schemas and metadata) over the time. To this extent, identifying suitable datasets, which meet specific criteria, has become an increasingly important, yet challenging task to supportissues such as entity retrieval or semantic search and data linking. Particularlywith respect to the interlinking issue, the current topology of the LOD cloud underlines the need for practical and efficient means to recommend suitable datasets: currently, only well-known reference graphs such as DBpedia (the most obvious target), YAGO or Freebase show a high amount of in-links, while there exists a long tail of potentially suitable yet under-recognized datasets. This problem is due to the semantic web tradition in dealing with "finding candidate datasets to link to", where data publishers are used to identify target datasets for interlinking.While an understanding of the nature of the content of specific datasets is a crucial prerequisite for the mentioned issues, we adopt in this dissertation the notion of "dataset profile" - a set of features that describe a dataset and allow the comparison of different datasets with regard to their represented characteristics. Our first research direction was to implement a collaborative filtering-like dataset recommendation approach, which exploits both existing dataset topic proles, as well as traditional dataset connectivity measures, in order to link LOD datasets into a global dataset-topic-graph. This approach relies on the LOD graph in order to learn the connectivity behaviour between LOD datasets. However, experiments have shown that the current topology of the LOD cloud group is far from being complete to be considered as a ground truth and consequently as learning data.Facing the limits the current topology of LOD (as learning data), our research has led to break away from the topic proles representation of "learn to rank" approach and to adopt a new approach for candidate datasets identication where the recommendation is based on the intensional profiles overlap between differentdatasets. By intensional profile, we understand the formal representation of a set of schema concept labels that best describe a dataset and can be potentially enriched by retrieving the corresponding textual descriptions. This representation provides richer contextual and semantic information and allows to compute efficiently and inexpensively similarities between proles. We identify schema overlap by the help of a semantico-frequential concept similarity measure and a ranking criterion based on the tf*idf cosine similarity. The experiments, conducted over all available linked datasets on the LOD cloud, show that our method achieves an average precision of up to 53% for a recall of 100%. Furthermore, our method returns the mappings between the schema concepts across datasets, a particularly useful input for the data linking step.In order to ensure a high quality representative datasets schema profiles, we introduce Datavore| a tool oriented towards metadata designers that provides rankedlists of vocabulary terms to reuse in data modeling process, together with additional metadata and cross-terms relations. The tool relies on the Linked Open Vocabulary (LOV) ecosystem for acquiring vocabularies and metadata and is made available for the community
Rossiello, Roberto. "Generazione di dataset RDF su articoli scientifici e affiliazioni: un approccio modulare basato su DBPedia." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8933/.
Повний текст джерелаAbbas, Nacira. "Formal Concept Analysis for Discovering Link Keys in the Web of Data." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0202.
Повний текст джерелаThe Web of data is a global data space that can be seen as an additional layer interconnected with the Web of documents. Data interlinking is the task of discovering identity links across RDF (Resource Description Framework) datasets over the Web of data. We focus on a specific approach for data interlinking, which relies on the “link keys”. A link key has the form of two sets of pairs of properties associated with a pair of classes. For example the link key ({(designation,title)},{(designation,title) (creator,author)},(Book,Novel)), states that whenever an instance “a” of the class “Book” and “b” of the class “Novel”, share at least one value for the properties “creator” and “author” and that, “a” and “b” have the same values for the properties “designation” and “title”, then “a” and “b” denote the same entity. Then (a,owl:sameAs,b) is an identity link over the two datasets. However, link keys are not always provided, and various algorithms have been developed to automatically discover these keys. First, these algorithms focus on finding “link key candidates”. The quality of these candidates is then evaluated using appropriate measures, and valid link keys are selected accordingly. Formal Concept Analysis (FCA) has been closely associated with the discovery of link key candidates, leading to the proposal of an FCA-based algorithm for this purpose. Nevertheless, existing algorithms for link key discovery have certain limitations. First, they do not explicitly specify the associated pairs of classes for the discovered link key candidates, which can lead to inaccurate evaluations. Additionally, the selection strategies employed by these algorithms may also produce less accurate results. Furthermore, redundancy is observed among the sets of discovered candidates, which presents challenges for their visualization, evaluation, and analysis. To address these limitations, we propose to extend the existing algorithms in several aspects. Firstly, we introduce a method based on Pattern Structures, an FCA generalization that can handle non-binary data. This approach allows for explicitly specifying the associated pairs of classes for each link key candidate. Secondly, based on the proposed Pattern Structure, we present two methods for link key selection. The first method is guided by the associated pairs of classes of link keys, while the second method utilizes the lattice generated by the Pattern Structure. These two methods improve the selection compared to the existing strategy. Finally, to address redundancy, we introduce two methods. The first method involves Partition Pattern Structure, which identifies and merges link key candidates that generate the same partitions. The second method is based on hierarchical clustering, which groups candidates producing similar link sets into clusters and selects a representative for each cluster. This approach effectively minimizes redundancy among the link key candidates
Barnathan, Michael. "Mining Complex High-Order Datasets." Diss., Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/82058.
Повний текст джерелаPh.D.
Selection of an appropriate structure for storage and analysis of complex datasets is a vital but often overlooked decision in the design of data mining and machine learning experiments. Most present techniques impose a matrix structure on the dataset, with rows representing observations and columns representing features. While this assumption is reasonable when features are scalar and do not exhibit co-dependence, the matrix data model becomes inappropriate when dependencies between non-target features must be modeled in parallel, or when features naturally take the form of higher-order multilinear structures. Such datasets particularly abound in functional medical imaging modalities, such as fMRI, where accurate integration of both spatial and temporal information is critical. Although necessary to take full advantage of the high-order structure of these datasets and built on well-studied mathematical tools, tensor analysis methodologies have only recently entered widespread use in the data mining community and remain relatively absent from the literature within the biomedical domain. Furthermore, naive tensor approaches suffer from fundamental efficiency problems which limit their practical use in large-scale high-order mining and do not capture local neighborhoods necessary for accurate spatiotemporal analysis. To address these issues, a comprehensive framework based on wavelet analysis, tensor decomposition, and the WaveCluster algorithm is proposed for addressing the problems of preprocessing, classification, clustering, compression, feature extraction, and latent concept discovery on large-scale high-order datasets, with a particular emphasis on applications in computer-assisted diagnosis. Our framework is evaluated on a 9.3 GB fMRI motor task dataset of both high dimensionality and high order, performing favorably against traditional voxelwise and spectral methods of analysis, discovering latent concepts suggestive of subject handedness, and reducing space and time complexities by up to two orders of magnitude. Novel wavelet and tensor tools are derived in the course of this work, including a novel formulation of an r-dimensional wavelet transform in terms of elementary tensor operations and an enhanced WaveCluster algorithm capable of clustering real-valued as well as binary data. Sparseness-exploiting properties are demonstrated and variations of core algorithms for specialized tasks such as image segmentation are presented.
Temple University--Theses
Koufakou, Anna. "SCALABLE AND EFFICIENT OUTLIER DETECTION IN LARGE DISTRIBUTED DATA SETS WITH MIXED-TYPE ATTRIBUTES." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3431.
Повний текст джерелаPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering PhD
Marcelli, Fulvio. "Estrazione automatica di informazioni da articoli scientifici in formato PDF e pubblicazione in Linked Open Data." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10892/.
Повний текст джерелаYang, Chaozheng. "Sufficient Dimension Reduction in Complex Datasets." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/404627.
Повний текст джерелаPh.D.
This dissertation focuses on two problems in dimension reduction. One is using permutation approach to test predictor contribution. The permutation approach applies to marginal coordinate tests based on dimension reduction methods such as SIR, SAVE and DR. This approach no longer requires calculation of the method-specific weights to determine the asymptotic null distribution. The other one is through combining clustering method with robust regression (least absolute deviation) to estimate dimension reduction subspace. Compared with ordinary least squares, the proposed method is more robust to outliers; also, this method replaces the global linearity assumption with the more flexible local linearity assumption through k-means clustering.
Temple University--Theses
Sherif, Mohamed Ahmed Mohamed [Verfasser], Klaus-Peter [Akademischer Betreuer] Fähnrich, Klaus-Peter [Gutachter] Fähnrich, Jens [Akademischer Betreuer] Lehmann, Ngomo Axel-Cyrille [Akademischer Betreuer] Ngonga, Sören [Akademischer Betreuer] Auer, and Daniel [Gutachter] Mirankar. "Automating Geospatial RDF Dataset Integration and Enrichment / Mohamed Ahmed Mohamed Sherif ; Gutachter: Klaus-Peter Fähnrich, Daniel Mirankar ; Klaus-Peter Fähnrich, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Sören Auer." Leipzig : Universitätsbibliothek Leipzig, 2016. http://d-nb.info/1240696035/34.
Повний текст джерелаLowry, Kimberly. "THE PATHS TO BECOMING A MATHEMATICS TEACHER." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3810.
Повний текст джерелаPh.D.
Department of Teaching and Learning Principles
Education
Mathematics Education
Bedeschi, Luca. "Analisi sulla crescita e sulle funzioni dei Linked Open Data - LODStories." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7733/.
Повний текст джерелаOrtiz, Enrique. "A Scalable and Efficient Outlier Detection Strategy for Categorical Data." Honors in the Major Thesis, University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1185.
Повний текст джерелаBachelors
Engineering and Computer Science
Computer Engineering
"Referring Expression Comprehension for CLEVR-Ref+ Dataset." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.62696.
Повний текст джерелаDissertation/Thesis
Masters Thesis Computer Science 2020
Jareš, Antonín. "Zjednodušení přístupu k propojeným datům pomocí tabulkových pohledů." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-451054.
Повний текст джерелаSoderi, Mirco. "Semantic models for the modeling and management of big data in a smart city environment." Doctoral thesis, 2021. http://hdl.handle.net/2158/1232245.
Повний текст джерела