MIRROR: Automatic R2RML Mapping Generation from Relational Databases

Medeiros, Luciano Frontino de; Priyatna, Freddy; Corcho, Óscar

doi:10.1007/978-3-319-19890-3_21

Cited by 46 publications

(37 citation statements)

References 6 publications

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“…R2RML implementations often provide a tool to automatically generate an R2RML direct mapping from the relational schema (e.g. Morph-RDB [29]). The same principles could be extended to automatically generate an xR2RML mapping for other types of data source, as long as they comply with a schema: column names in CSV/TSV files and extensible column stores, XSD or DTD for XML data, JSON schema 22 or a JSON-LD 23 description for JSON data.…”

Section: Discussionmentioning

confidence: 99%

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

Michel

Djimenou

Faron-Zucker

et al. 2016

Lecture Notes in Business Information Processing

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Abstract. While the data deluge accelerates, most of the data produced remains locked in deep Web databases. For the linked open data to benefit from the potential represented by this huge amount of data, it is crucial to come up with solutions to expose heterogeneous databases as linked data. The xR2RML mapping language is an endeavor towards this goal: it is designed to map various types of databases to RDF, by flexibly adapting to heterogeneous query languages and data models while remaining free from any specific language. It extends R2RML, the W3C recommendation for the mapping of relational databases to RDF, and relies on RML for the handling of various data formats. In this paper we present xR2RML, we analyse data models of several modern databases as well as the format in which query results are returned, and we show how xR2RML translates any result data element into RDF, relying on existing languages such as XPath and JSONPath when necessary. We illustrate some features of xR2RML such as the generation of RDF collections and containers, and the ability to deal with mixed data formats. We also describe a real-world use case in which we applied xR2RML to build a SKOS thesaurus aimed at supporting studies on History of Zoology, Archaeozoology and Conservation Biology.

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Section: Discussionmentioning

confidence: 99%

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

Michel

Djimenou

Faron-Zucker

et al. 2016

Lecture Notes in Business Information Processing

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“…Systems like MIRROR [21] and BootOX [27] support automatic generation of such complex mappings by exploiting the common patterns that schema managers use to encode the semantics of the domain in the relational database.…”

Section: Mapping Bootstrappers Automatically Generate Mappings and Vomentioning

confidence: 99%

“…Morph-RDB, 21 formerly called ODEMapster, is an open-source OBDA system supporting the R2RML and Direct Mappings standards. This system implements a number of query optimizations techniques such as self-join elimination [44].…”

Section: Ultrawrapmentioning

confidence: 99%

Ontop: Answering SPARQL queries over relational databases

et al. 2016

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Abstract. We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL 2 QL and RDFS ontologies), and its support for all major relational databases.

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“…The alignment between the bootstrap and target ontology is done with schema matching tools, such as LogMap [13]. MIRROR [14] and Ontop [15] are similar to D2RQ. However, MIRROR extends the mappings by using information in the databases to determine, e.g., subclass-of relationships and m:n relationships.…”

Section: Mapping Generation Toolsmentioning

confidence: 99%

“…In most cases, when tools are accompanied with an evaluation, they only assess a limited set of the mapping process' aspects: time required to transform the mapping suggestions to the correct ones [11]; W3C Direct Mapping Test Cases 2 [14]; precision, recall, and CPU time when using the semantic model [9] and Linked Data [8]; or number of user actions [21]. However, they only give a limited insight about task complexity and/or mapping quality, and not every tool is compared with the results of other tools.…”

Section: Mapping Generation Evaluationmentioning

confidence: 99%

Ontology-Based Data Access Mapping Generation Using Data, Schema, Query, and Mapping Knowledge

et al. 2017

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Abstract. Ontology-Based Data Access systems provide access to nonrdf data using ontologies. These systems require mappings between the non-rdf data and ontologies to facilitate this access. Manually defining such mappings can become a costly process when dealing with large and complex data sources, and/or multiple data sources at the same time. This resulted in different mapping generation tools. While a number of these tools use knowledge from the original data, existing Linked Data, schemas, and/or mappings, they still fall short when dealing with complex challenges and the user effort can be high. In this paper, we propose an approach, together with an evaluation, that discovers and uses extended knowledge from existing (Linked) Data, schemas, query workload, and mappings, and combines it with knowledge provided by the mapping process to generate a new mapping. Our approach aims to improve the mapping quality, while decreasing the task complexity, and subsequently the user effort.

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MIRROR: Automatic R2RML Mapping Generation from Relational Databases

Cited by 46 publications

References 6 publications

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

Ontop: Answering SPARQL queries over relational databases

Ontology-Based Data Access Mapping Generation Using Data, Schema, Query, and Mapping Knowledge

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