Academic literature on the topic 'RDB2RDF mapping'

Direct mapping is an automatic transformation method used to generate resource description framework (RDF) data from relational data. In the field of direct mapping, semantics preservation is critical to ensure that the mapping method outputs RDF data without information loss or incorrect semantic data generation. However, existing direct-mapping methods have problems that prevent semantics preservation in specific cases. For this reason, a mapping method is developed to perform a semantics-preserving transformation of relational databases (RDB) into RDF data without semantic information loss and to reduce the volume of incorrect RDF data. This research reviews cases that do not generate semantics-preserving results, and the corresponding problems into categories are arranged. This paper defines lemmas that represent the features of RDF data transformation to resolve those problems. Based on the lemmas, this work develops a hierarchical direct-mapping method to strictly abide by the definition of semantics preservation and to prevent semantic information loss, reducing the volume of incorrect RDF data generated. Experiments demonstrate the capability of the proposed method to perform semantics-preserving RDB2RDF data transformation, generating semantically accurate results. This work impacts future studies, which should involve the development of synchronization methods to achieve RDF data consistency when original RDB data are modified.

Background/Objectives: The mapping RDB to RDF has become important to populate Linked Data more efficiently. This paper shows how to implement SPARQL endpoint in RDB using a conceptual level mapping approach.Methods/Statistical analysis: Many diverse approaches and related languages for mapping RDB to RDF have been proposed. The prominent achievements of mapping RDB to RDF are two standard draft Direct Mapping and R2RML proposed by W3C RDB2RDF Working Group. This paper analyzes these conventional mapping approaches and proposes a new approach based on schema mapping. The paper also presents SPARQL query processing in RDB.Findings: There are distinct differences between instance level mapping and conceptual level mapping for RDB2RDF. Data redundancy of instance level mapping causes many inevitable problems during mapping procedure. The conceptual level mapping can provide straightforward and efficient way. The ER model in RDB and RDF model in Linked Data have obvious similarity. The ER model describes entities and relationships, which is the conceptual schema of RDB. RDF model consists of three parts: subject, predicate and object, which is the standard model for data interchange on the Web. The entities in ER model and subjects in RDF model are all the things that can be anything in the real world. Both the relationships in ER model and predicates in RDF model describe the relations between things.Since RDB and RDF share the similar modeling approach at the schema level, it is reasonable that mapping approach should be based on RDB schema. This kind of conceptual level mapping also can provide efficient SPARQL query processing in RDB.Improvements/Applications: The paper realizes SPARQL query processing in RDB, which is based on conceptual level mapping. The query experiments show that it is a concise and efficient way to populate Linked Data.

Cette thèse étudie le problème de la protection de la vie privée dans le LinkedOpen Data (ou « LOD », en français « web des données ouvertes » ou encore « donnéesliées ouvertes »). Ce travail se situe à l’intersection d’une longue série de travaux sur laconfidentialité des données et le LOD. Notre objectif est d’étudier l’impact des aspectssémantiques sur la publication des données et sur les fuites éventuelles d’information.Nous considérons RDF comme le format de représentation du LOD et la confidentialitédifférentielle (DP) comme le principal critère de protection de la vie privée. La DP a étéinitialement conçue pour définir la confidentialité dans le domaine des bases de donnéesrelationnelle. Elle est basée sur une quantification de la difficulté pour un attaquantd’identifier, en observant le résultat d’un algorithme, quelle base de données parmis unvoisinage a été utilisée pour le produire.Les objectifs de cette thèse sont au nombre de quatre: O1) améliorer la protectiondes données LOD. En particulier, proposer une approache permettant de construire desméchanismes DP utilisables sur RDF ; O2) étudier comment les définitions des voisinagessur les bases de données relationnelles en présence de contraintes de clés étrangères (FK) peuvent être traduites en RDF : O3) proposer de nouvelles définitions de voisinages sur des bases de données relationnelles équivalente à des notions existantes de voisinage sur les graphes (avec une sémantique précise) et O4) proposer un formalisme facilitant laconception et l’implémentation de mécanismes d’anonymisation de données RDF.Concernant O1, nous proposons une nouvelle approche basée sur la projection degraphes pour adapter le concept de DP à RDF. Pour O2, nous déterminons le modèlede protection qui correspond à la traduction de modèles déjà existants pour des basesde données relationnelles sous contraintes FK. Pour O3, nous introduisons le conceptde restrict deletion neighborhood (voisinage d’effacement limité) équivalent envoisinage de type "typed-node" (noeud typé). Nous proposons également une relaxation de la définition permettant de traduite les voisinages "typed-outedge" (arc sortanttypé). Pour O4, nous proposons un langage de transformation de graphes basé sur leconcept de réécriture de graphes, qui sert de fondation pour construire divers mécanismes d’anonymisation sur des graphes attribués.L’ensemble de nos contributions théoriques ont été implémentées par des prototypes"preuve de concept" et ont été évalués sur des jeux de données réels, afin de montrerl’applicabilité de nos travaux à des cas d’usage réels
This 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

Today, the Web of Data evolved to a semantic information network containing large amounts of data. Since such data may stem from different sources, ranging from automatic extraction processes to extensively curated knowledge bases, its quality also varies. Thus, currently research efforts are made to find methodologies and approaches to measure the data quality in the Web of Data. Besides the option to consider the actual data in a quality assessment, taking the process of data generation into account is another possibility, especially for extracted data. An extraction approach that gained popularity in the last years is the mapping of relational databases to RDF (RDB2RDF). By providing definitions of how RDF should be generated from relational database content, huge amounts of data can be extracted automatically. Unfortunately, this also means that single errors in the mapping definitions can affect a considerable portion of the generated data. Thus, from a quality assurance point of view, the assessment of these RDB2RDF mapping definitions is important to guarantee high quality RDF data. This is not covered by recent quality research attempts in depth and is examined in this thesis. After a structured evaluation of existing approaches, a quality assessment methodology and quality dimensions of importance for RDB2RDF mappings are proposed. The formalization of this methodology is used to define 43 metrics to characterize the quality of an RDB2RDF mapping project. These metrics are also implemented for a software prototype of the proposed methodology, which is used in a practical evaluation of three different datasets that are generated applying the RDB2RDF approach.