Extending R2RML to a source-independent mapping language for RDF

Abstract

Although reaching the fth star of the Open Data deployment scheme demands the data to be represented in RDF and linked, a generic and standard mapping procedure to deploy raw data in RDF was not established so far. Only the R2RML mapping language was standardized but its applicability is limited to mappings from relational databases to RDF. We propose the extension of R2RML to also support mappings of data sources in other structured formats (indicatively CSV, TSV, XML, JSON). Broadening further its scope, the focus is put on the mappings and their optimal reuse. The language becomes sourceagnostic, and resources are integrated and interlinked at a primary stage.

Introduction

State of the art

Beyond R2RML which has already several implementations3, other RDB2RDF mapping languages were de ned [ 1 ]. In the same context, there are corresponding languages to support CSV-to-RDF mappings (CSV2RDF), e.g., the XLWrap's mapping language [ 2 ], the Mapping Master's M2 [ 3 ] and Vertere4. On the other hand, in the case of mappings from XML to RDF (XML2RDF), the di erent tools rely mostly on existing XML solutions. To be more precise, XSLT-based approaches were explored, as the Krextor [ 4 ] and the AstroGrid-D5 mapping tools, while other implementations deploy mappings using XPath and XQuery, e.g., the Tripliser6 and the XSPARQL [ 5 ]. These solutions for XML sources lead to mappings on the syntactic level rather than on the semantic level or fail to provide a solution applicable to a broader domain. Beyond the standard ExtractMap-Load (EML) mappings, dynamic query translation was also explored, e,g, in the case of Tarql7 (CSV2RDF) and XSPARQL (mapping and integration of XML, RDB and RDF resources).

In general, most tools deploy mappings from a certain source format to RDF (source-centric approaches ). There are only a few tools that provide mappings from various source formats to RDF {DataLift [ 6 ], the DataTank [ 7 ], Karma [ 8 ], Open Re ne8 and Virtuoso Sponger9 are the most well known{ but only the DataTank uses a mapping language. For the latter's needs, Vertere was extended not only to cover CSV2RDF mappings but mappings from other structured data sources as well, namely databases, XML and JSON. Since R2RML became a W3C standard and due to its analogous nature to Vertere, the extension of R2RML is considered a prominent solution and its applicability veri ed. 3

Extending R2RML for a more generic use

An extension of the R2RML language is proposed, aiming to broaden its scope beyond RDB2RDF mappings, to cover every structured data format (a GlobalAs-View approach), and to address the limitations of existing languages. The R2RML's RDF graphs are used to express mappings independently of the source format. Therefore, the same custom mappings are reused whether the source les are in the same format or not, only by redetermining the references to the source values to be mapped, as the expected custom mapping de nitions remain the same. The vocabulary extending the R2RML is available at http://mmlab.be/users/andimou/rml.ttl. The expansion is achieved as follows: 3 http://www.w3.org/2001/sw/rdb2rdf/wiki/Implementations 4 https://github.com/knudmoeller/Vertere-RDF 5 http://www.gac-grid.de/project-products/Software/XML2RDF.html 6 http://daverog.github.io/tripliser/ 7 https://github.com/cygri/tarql 8 http://openre ne.org/ 9 http://virtuoso.openlinksw.com/dataspace/doc/dav/wiki/Main/VirtSponger Extending RDF triples mapping. Triples map is extended not only to map each row in the logical table, but each resource in the logical source. To this end, the rr:logicalTable and rr:tableName become a sub-property of rml:logicalSource and rml:sourceName respectively, while rr:elementName for XML sources and rr:objectName for JSON sources are introduced. In the example, books is a logical table's, a JSON object's or an XML element's name. <# RDB_CSV_map > rml : logicalSource [ rr: tableName " BOOKS " ]; rr: subjectMap [ rr: template " http :// data . example . com / books /{ ISBN }" ]; rr: predicateObjectMap [ rr: predicate ex:id; rr: objectMap [ rml : resource "ID" ] ]. <# XML_map > rml : logicalSource [ rml : elementName "/ books " ]; rr: subjectMap [ rr: template " http :// data . example . com / books /{ book / ISBN }"]; rr: predicateObjectMap [ rr: predicate ex:id; rr: objectMap [ rml : resource " book / ISBN@id " ] ]. <# JSON_map > rml : logicalSource [ rml : objectName " books " ]; rr: subjectMap [ rr: template " http :// data . example . com / books /{ book . ISBN }"]; rr: predicateObjectMap [ rr: predicate ex:id; rr: objectMap [ rml : resource " book .id" ] ]. Extending resources' mapping. In the same context, term maps are extended to generate RDF terms from any logical resource, either this is a table row, an XML element or a JSON object. The column-valued term map is extended to cover every resource term map. Therefore, the R2RML's rr:column property becomes a sub-property of the rml:resource which is a valid column name for relational databases and CSV les, a valid XPath expression for an XML node's or attribute's absolute path and a valid path pattern in JavaScript syntax for objects in JSON source les, as in the aforementioned example. Multiple entities per row. Most of the mapping languages (including R2RML) follow the entity-per-row model and consider that each row's RDF triples are mapped to the same subject. In its extended version, R2RML can map sets of columns to di erent subjects, which are then related among each other with a predicate-object triples map. For example, a row may have several columns with information about an event and a few of them refer to its location, e.g., latitude and longitude. Using this single row a triples map may be de ned for the event while another triples map may be de ned for the location where the event takes place (this mapping de nition might be reused for other locations' mapping) and the two of them are related with a predicate-object triples map, as in the following example: <# Event_map > rml : logicalSource [ rml : elementName "/ events " ]; rr: subjectMap [ rr: template " http :// data . example . com / events /{ event /id }" ]; rr: predicateObjectMap [ rr: predicate ex: location ; rr: objectMap [ rr: parentTriplesMap <# Location_map > ] ] , [ rr: predicate ex: transport ; rr: objectMap [ rr: parentTriplesMap <# Transport_map > ; rr: joinCondition [ rr: child " event / bus / num "; rr: parent " BUS_NUM " ] ] ]. <# Location_map > rml : logicalSource [ rml : elementName "/ events " ]; rr: subjectMap [ rr: template " http :// data . example . com / location /{ event / location / lat } ,{ event / location / long }"]. <# Transport_map > rml : logicalSource [ rr: tableName " TRANSPORTATIONS " ]; rr: subjectMap [ rr: template " http :// data . example . com / transport /{ TYPE }/{ BUS_NUM }"]; rr: predicateObjectMap [ rr: predicate ex: name ; rr: objectMap [ rml : resource " BUS_NAME " ] ]. Extended the logical sources. According to the rr:sqlQuery, the rml:xmlQuery is adapted and both are sub-properties of rml:query to serve a query against a source le. In the same context the rml:queryLanguage is de ned to determine which language is used (indicatively, a W3C standard in the case of XML). Integrated mapping. Extending the reference object map, one can use the subjects of another triples map as the objects generated by a predicate-object map. Since the triples maps may be based on di erent logical sources, the potential to create triples based on integrated sources emerges. At the aforementioned event example, an element node may refer to the number of the bus going to the event location, but the bus names are associated to the bus numbers at a separate table which is mapped by another triples map. The mappings of both of them are de ned and a predicate-object terms map may be used to relate them. 4

Conclusions and Future Work

A generic mapping language is proposed to handle the mappings from di erent source formats to RDF. The uppermost goal of such an extension is to keep the focus on the mappings to be expressed rather than on the data and their original structure. With this work, we bring into discussion its feasibility, possible barriers and aspects that should be taken into consideration. In the future the arising generic mapping language will be used at the DataTank, instead of Vertere, to cover mappings from di erent source formats to RDF and, in the same time, to confront with the standard mapping language for the RDB2RDF mappings.

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