Literatura científica selecionada sobre o tema "Saturation des données RDF"
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Artigos de revistas sobre o assunto "Saturation des données RDF"
Colazzo, Dario, François Goasdoué, Ionna Manolescu e Alexandra Roatis. "Analyse de données RDF. Lentilles pour graphes sémantiques". Ingénierie des systèmes d'information 19, n.º 4 (28 de agosto de 2014): 87–117. http://dx.doi.org/10.3166/isi.19.4.87-117.
Texto completo da fonteFayçal, Hamdi, e Si-Said Cherfi Samira. "Une approche pour évaluer la complétude de données RDF". Ingénierie des systèmes d'information 21, n.º 3 (28 de junho de 2016): 31–52. http://dx.doi.org/10.3166/isi.21.3.31-52.
Texto completo da fonteGagnon, Michel. "Les bibliothèques numériques sont-elles solubles dans le Web sémantique ?" Documentation et bibliothèques 59, n.º 3 (11 de outubro de 2013): 161–68. http://dx.doi.org/10.7202/1018846ar.
Texto completo da fonteLe Boeuf, Patrick. "Modélisation conceptuelle de l’information bibliographique et muséologique : CIDOC CRM et FRBRoo". Documentation et bibliothèques 55, n.º 4 (18 de março de 2015): 209–14. http://dx.doi.org/10.7202/1029185ar.
Texto completo da fontePandeline, Deborah A., Paul J. Cosentino, Edward H. Kalajian e Mario F. Chavez. "Shear and Deformation Characteristics of Municipal Waste Combustor Bottom Ash for Highway Applications". Transportation Research Record: Journal of the Transportation Research Board 1577, n.º 1 (janeiro de 1997): 101–8. http://dx.doi.org/10.3141/1577-13.
Texto completo da fonteZamora, Jonathan, Israel Betancourt e José Alejandro García Hinojosa. "The Influence of Boron on Microstructural Evolution, Mechanical and Magnetic Behavior of Amorphous Fe91−x(Zr5-Nb4)Bx Melt-Spun Alloys". Metals 12, n.º 6 (10 de junho de 2022): 994. http://dx.doi.org/10.3390/met12060994.
Texto completo da fonteZIDAOUI, I., C. JOANNIS, J. WERTEL, S. ISEL, C. WEMMERT, J. VAZQUEZ e M. DUFRESNE. "Utilisation de l’intelligence artificielle pour la validation des mesures en continu de la pollution des eaux usées". Techniques Sciences Méthodes 11 (21 de novembro de 2022): 39–51. http://dx.doi.org/10.36904/tsm/202211039.
Texto completo da fonteCaty, Marie-Ève, e Maude Hébert. "Cheminement et difficultés analytiques en méthodologie de la théorisation enracinée : expérience de deux doctorantes". Approches inductives 6, n.º 1 (3 de junho de 2019): 61–90. http://dx.doi.org/10.7202/1060045ar.
Texto completo da fonteKrishtal, S., M. Kiselev, Y. Puhovski, T. Kerdcharoen, S. Hannongbua e K. Heinzinger. "Study of the Hydrogen Bond Network in sub-and supercritical Water by Molecular Dynamics Simulations". Zeitschrift für Naturforschung A 56, n.º 8 (1 de agosto de 2001): 579–84. http://dx.doi.org/10.1515/zna-2001-0807.
Texto completo da fonteSCIUTO, C., e R. ROUSSEAU. "Expression du désaccord par le patient en consultation de médecine générale". EXERCER 31, n.º 164 (1 de junho de 2020): 254–60. http://dx.doi.org/10.56746/exercer.2020.164.254.
Texto completo da fonteTeses / dissertações sobre o assunto "Saturation des données RDF"
Bouhamoum, Redouane. "Découverte automatique de schéma pour les données irrégulières et massives". Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG081.
Texto completo da fonteThe web of data is a huge global data space, relying on semantic web technologies, where a high number of sources are published and interlinked. This data space provides an unprecedented amount of knowledge available for novel applications, but the meaningful usage of its sources is often difficult due to the lack of schema describing the content of these data sources. Several automatic schema discovery approaches have been proposed, but while they provide good quality schemas, their use for massive data sources is a challenge as they rely on costly algorithms. In our work, we are interested in both the scalability and the incrementality of schema discovery approaches for RDF data sources where the schema is incomplete or missing.Furthermore, we extend schema discovery to take into account not only the explicit information provided by a data source, but also the implicit information which can be inferred.Our first contribution consists of a scalable schema discovery approach which extracts the classes describing the content of a massive RDF data source.We have proposed to extract a condensed representation of the source, which will be used as an input to the schema discovery process in order to improve its performances.This representation is a set of patterns, each one representing a combination of properties describing some entities in the dataset. We have also proposed a scalable schema discovery approach relying on a distributed clustering algorithm that forms groups of structurally similar entities representing the classes of the schema.Our second contribution aims at maintaining the generated schema consistent with the data source it describes, as this latter may evolve over time. We propose an incremental schema discovery approach that modifies the set of extracted classes by propagating the changes occurring at the source, in order to keep the schema consistent with its evolutions.Finally, the goal of our third contribution is to extend schema discovery to consider the whole semantics expressed by a data source, which is represented not only by the explicitly declared triples, but also by the ones which can be inferred through reasoning. We propose an extension allowing to take into account all the properties of an entity during schema discovery, represented either by explicit or by implicit triples, which will improve the quality of the generated schema
Lesnikova, Tatiana. "Liage de données RDF : évaluation d'approches interlingues". Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM011/document.
Texto completo da fonteThe Semantic Web extends the Web by publishing structured and interlinked data using RDF.An RDF data set is a graph where resources are nodes labelled in natural languages. One of the key challenges of linked data is to be able to discover links across RDF data sets. Given two data sets, equivalent resources should be identified and linked by owl:sameAs links. This problem is particularly difficult when resources are described in different natural languages.This thesis investigates the effectiveness of linguistic resources for interlinking RDF data sets. For this purpose, we introduce a general framework in which each RDF resource is represented as a virtual document containing text information of neighboring nodes. The context of a resource are the labels of the neighboring nodes. Once virtual documents are created, they are projected in the same space in order to be compared. This can be achieved by using machine translation or multilingual lexical resources. Once documents are in the same space, similarity measures to find identical resources are applied. Similarity between elements of this space is taken for similarity between RDF resources.We performed evaluation of cross-lingual techniques within the proposed framework. We experimentally evaluate different methods for linking RDF data. In particular, two strategies are explored: applying machine translation or using references to multilingual resources. Overall, evaluation shows the effectiveness of cross-lingual string-based approaches for linking RDF resources expressed in different languages. The methods have been evaluated on resources in English, Chinese, French and German. The best performance (over 0.90 F-measure) was obtained by the machine translation approach. This shows that the similarity-based method can be successfully applied on RDF resources independently of their type (named entities or thesauri concepts). The best experimental results involving just a pair of languages demonstrated the usefulness of such techniques for interlinking RDF resources cross-lingually
Tanasescu, Adrian. "Vers un accès sémantique aux données : approche basée sur RDF". Lyon 1, 2007. http://www.theses.fr/2007LYO10069.
Texto completo da fonteThe thesis mainly focuses on information retrival through RDF documents querying. Therefore, we propose an approach able to provide complete and pertinent answers to a user formulated SPARQL query. The approach mainly consists of (1) determining, through a similarity measure, whether two RDF graphs are contradictory, by using the associated ontological knowledge, and (2) building pertinent answers through the combination of statements belonging to non contradicting RDF graphs that partially answer a given query. We also present an RDF storage and querying platform, named SyRQuS, whose query answering plan is entirely based on the former proposed querying approach. SyRQuS is a Web based plateform that mainly provides users with a querying interface where queries can be formulated using SPARQL
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.
Texto completo da fonteWith 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
Ouksili, Hanane. "Exploration et interrogation de données RDF intégrant de la connaissance métier". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLV069.
Texto completo da fonteAn increasing number of datasets is published on the Web, expressed in languages proposed by the W3C to describe Web data such as RDF, RDF(S) and OWL. The Web has become a unprecedented source of information available for users and applications, but the meaningful usage of this information source is still a challenge. Querying these data sources requires the knowledge of a formal query language such as SPARQL, but it mainly suffers from the lack of knowledge about the source itself, which is required in order to target the resources and properties relevant for the specific needs of the application. The work described in this thesis addresses the exploration of RDF data sources. This exploration is done according to two complementary ways: discovering the themes or topics representing the content of the data source, and providing a support for an alternative way of querying the data sources by using keywords instead of a query formulated in SPARQL. The proposed exploration approach combines two complementary strategies: thematic-based exploration and keyword search. Theme discovery from an RDF dataset consists in identifying a set of sub-graphs which are not necessarily disjoints, and such that each one represents a set of semantically related resources representing a theme according to the point of view of the user. These themes can be used to enable a thematic exploration of the data source where users can target the relevant theme and limit their exploration to the resources composing this theme. Keyword search is a simple and intuitive way of querying data sources. In the case of RDF datasets, this search raises several problems, such as indexing graph elements, identifying the relevant graph fragments for a specific query, aggregating these relevant fragments to build the query results, and the ranking of these results. In our work, we address these different problems and we propose an approach which takes as input a keyword query and provides a list of sub-graphs, each one representing a candidate result for the query. These sub-graphs are ordered according to their relevance to the query. For both keyword search and theme identification in RDF data sources, we have taken into account some external knowledge in order to capture the users needs, or to bridge the gap between the concepts invoked in a query and the ones of the data source. This external knowledge could be domain knowledge allowing to refine the user's need expressed by a query, or to refine the definition of themes. In our work, we have proposed a formalization to this external knowledge and we have introduced the notion of pattern to this end. These patterns represent equivalences between properties and paths in the dataset. They are evaluated and integrated in the exploration process to improve the quality of the result
Michel, Franck. "Intégrer des sources de données hétérogènes dans le Web de données". Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4002/document.
Texto completo da fonteTo a great extent, the success of the Web of Data depends on the ability to reach out legacy data locked in silos inaccessible from the web. In the last 15 years, various works have tackled the problem of exposing various structured data in the Resource Description Format (RDF). Meanwhile, the overwhelming success of NoSQL databases has made the database landscape more diverse than ever. NoSQL databases are strong potential contributors of valuable linked open data. Hence, the object of this thesis is to enable RDF-based data integration over heterogeneous data sources and, in particular, to harness NoSQL databases to populate the Web of Data. We propose a generic mapping language, xR2RML, to describe the mapping of heterogeneous data sources into an arbitrary RDF representation. xR2RML relies on and extends previous works on the translation of RDBs, CSV/TSV and XML into RDF. With such an xR2RML mapping, we propose either to materialize RDF data or to dynamically evaluate SPARQL queries on the native database. In the latter, we follow a two-step approach. The first step performs the translation of a SPARQL query into a pivot abstract query based on the xR2RML mapping of the target database to RDF. In the second step, the abstract query is translated into a concrete query, taking into account the specificities of the database query language. Great care is taken of the query optimization opportunities, both at the abstract and the concrete levels. To demonstrate the effectiveness of our approach, we have developed a prototype implementation for MongoDB, the popular NoSQL document store. We have validated the method using a real-life use case in Digital Humanities
Rihany, Mohamad. "Keyword Search and Summarization Approaches for RDF Dataset Exploration". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG030.
Texto completo da fonteAn 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
Lozano, Aparicio Jose Martin. "Data exchange from relational databases to RDF with target shape schemas". Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1I063.
Texto completo da fonteResource Description Framework (RDF) is a graph data model which has recently found the use of publishing on the web data from relational databases. We investigate data exchange from relational databases to RDF graphs with target shapes schemas. Essentially, data exchange models a process of transforming an instance of a relational schema, called the source schema, to a RDF graph constrained by a target schema, according to a set of rules, called source-to-target tuple generating dependencies. The output RDF graph is called a solution. Because the tuple generating dependencies define this process in a declarative fashion, there might be many possible solutions or no solution at all. We study constructive relational to RDF data exchange setting with target shapes schemas, which is composed of a relational source schema, a shapes schema for the target schema, a set of mappings that uses IRI constructors. Furthermore, we assume that any two IRI constructors are non-overlapping. We propose a visual mapping language (VML) that helps non-expert users to specify mappings in this setting. Moreover, we develop a tool called ShERML that performs data exchange with the use of VML and for users that want to understand the model behind VML mappings, we define R2VML, a text-based mapping language, that captures VML and presents a succinct syntax for defining mappings.We investigate the problem of checking consistency: a data exchange setting is consistent if for every input source instance, there is at least one solution. We show that the consistency problem is coNP-complete and provide a static analysis algorithm of the setting that allows to decide if the setting is consistent or not. We study the problem of computing certain answers. An answer is certain if the answer holds in every solution. Typically, certain answers are computed using a universal solution. However, in our setting a universal solution might not exist. Thus, we introduce the notion of universal simulation solution, which always exists and allows to compute certain answers to any class of queries that is robust under simulation. One such class is nested regular expressions (NREs) that are forward i.e., do not use the inverse operation. Using universal simulation solution renders tractable the computation of certain answers to forward NREs (data-complexity).Finally, we investigate the shapes schema elicitation problem that consists of constructing a target shapes schema from a constructive relational to RDF data exchange setting without the target shapes schema. We identity two desirable properties of a good target schema, which are soundness i.e., every produced RDF graph is accepted by the target schema; and completeness i.e., every RDF graph accepted by the target schema can be produced. We propose an elicitation algorithm that is sound for any schema-less data exchange setting, but also that is complete for a large practical class of schema-less settings
Kellou-Menouer, Kenza. "Découverte de schéma pour les données du Web sémantique". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV047/document.
Texto completo da fonteAn increasing number of linked data sources are published on the Web. However, their schema may be incomplete or missing. In addition, data do not necessarily follow their schema. This flexibility for describing the data eases their evolution, but makes their exploitation more complex. In our work, we have proposed an automatic and incremental approach enabling schema discovery from the implicit structure of the data. To complement the description of the types in a schema, we have also proposed an approach for finding the possible versions (patterns) for each of them. It proceeds online without having to download or browse the source. This can be expensive or even impossible because the sources may have some access limitations, either on the query execution time, or on the number of queries.We have also addressed the problem of annotating the types in a schema, which consists in finding a set of labels capturing their meaning. We have proposed annotation algorithms which provide meaningful labels using external knowledge bases. Our approach can be used to find meaningful type labels during schema discovery, and also to enrichthe description of existing types.Finally, we have proposed an approach to evaluate the gap between a data source and itsschema. To this end, we have proposed a setof quality factors and the associated metrics, aswell as a schema extension allowing to reflect the heterogeneity among instances of the sametype. Both factors and schema extension are used to analyze and improve the conformity between a schema and the instances it describes
Taki, Sara. "Anonymisation de données liées en utilisant la confidentialité différentielle". Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2023. http://www.theses.fr/2023ISAB0009.
Texto completo da fonteThis thesis studies the problem of privacy in linked open data (LOD). Thiswork is at the intersection of long lines of work on data privacy and linked open data.Our goal is to study how the presence of semantics impacts the publication of data andpossible data leaks. We consider RDF as the format to represent LOD and DifferentialPrivacy (DP) as the main privacy concept. DP was initially conceived to define privacyin the relational database (RDB) domain and is based on a quantification of the difficultyfor an attacker observing an output to identify which database among a neighborhoodis used to produce it.The objective of this thesis is four-fold: O1) to improve the privacy of LOD. Inparticular, to propose an approach to construct usable DP-mechanisms on RDF; O2) tostudy how neighborhood definitions over RDB in the presence of foreign key (FK) constraints translate to RDF; O3) to propose new neighborhood definitions over relationaldatabase translating into existing graph concepts to ease the design of DP mechanisms;and O4) to support the implementation of sanitization mechanisms for RDF graphs witha rigorous formal foundation.For O1, we propose a novel approach based on graph projection to adapt DP toRDF. For O2, we determine the privacy model resulting from the translation of popularprivacy model over RDB with FK constraints to RDF. For O3, we propose the restrictdeletion neighborhood over RDB with FK constraints whose translation to the RDFgraph world is equivalent to typed-node neighborhood. Moreover, we propose a looserdefinition translating to typed-outedge neighborhood. For O4, we propose a graphtransformation language based on graph rewriting to serve as a basis for constructingvarious sanitization mechanisms on attributed graphs.We support all our theoretical contributions with proof-of-concept prototypes thatimplement our proposals and are evaluated on real datasets to show the applicability ofour work
Capítulos de livros sobre o assunto "Saturation des données RDF"
Farvardin, Mohammad Amin, Dario Colazzo, Khalid Belhajjame e Carlo Sartiani. "Scalable Saturation of Streaming RDF Triples". In Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV, 1–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62271-1_1.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Saturation des données RDF"
Farvardin, Mohammad Amin, Dario Colazzo, Khalid Belhajjame e Carlo Sartiani. "Streaming saturation for large RDF graphs with dynamic schema information". In the 17th ACM SIGPLAN International Symposium. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3315507.3330201.
Texto completo da fonteRIBEIRO DOS SANTOS, Daniel, Anne JULIEN-VERGONJANNE e Johann BOUCLÉ. "Cellules Solaires pour les Télécommunications et la Récupération d’Énergie". In Les journées de l'interdisciplinarité 2022. Limoges: Université de Limoges, 2022. http://dx.doi.org/10.25965/lji.661.
Texto completo da fonte