R2LD: Schema-based Graph Mapping of relational databases to Linked Open Data for multimedia resources data

Zhanfang Zhao, SungKook Han, JuRi Kim
<span title="2019-02-07">2019</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7inqmh346zfjjizjyieh7ijtma" style="color: black;">Multimedia tools and applications</a> </i> &nbsp;
The Web of Data used to share and interchange the diverse data of heterogeneous types on the Web has been actively established. Ontology-based Linked Open Data (LOD) that allows computers to understand and process data semantics has emerged to extend the current Web of Documents. LOD is important for the modeling of multimedia resources since it provides an efficient way for unstructured data resources. This paper proposes a noble and practical schema-based mapping method to populate Linked
more &raquo; ... Data sets from relational databases for multimedia resources. The proposed schema-based mapping R2LD realizes the seamless mapping for RDB-to-RDF by taking advantage of the compatible conceptual schema. The schema-based mapping can resolve the complicated mapping issues in RDB-to-RDF, such as primary and foreign key relationships. The mapping description is straightforward and flexible. It can define mapping information in the form of attribute-value pairs. Especially, the proposed mapping method is suitable for the unstructured multimedia resources. The schema-based mapping R2LD provides an efficient way to implement SPARQL endpoint into RDB and preserve the performance of SQL, which is vital to the dissemination of LOD. extend the current Web of Documents to a Web of interlinked data and, ultimately, into the Semantic Web. Using domain ontologies, LOD can exploit machine-readable data from the diverse data resources in the Web by means of web-based standards for encoding datasets and linking them to other published datasets. In the last decade, numerous best practices of LOD, including DBpedia,YAGO and CAMO, have been published by an increasing number of researchers, governments, public organizations and data providers, creating a global data space that interlinks billions of assertions: the Web of Linked Open Data [4, 7, 9] . Therefore, LOD has evolved from a practical research idea into a very promising technology that can realize the Web as a platform for an intelligent information system with semantic search, query and reasoning capability. In order to accelerate LOD paradigm in the Web, the publication of machine-readable LOD sets should take precedence [4, 21] . Since the vast amount of useful data are still stored in relational databases (RDB), one of the most efficient ways to populate LOD sets is to map data in relational databases into RDF, which is the standard data model of LOD. Due to the importance of mapping RDB to RDF (RDB2RDF), the multifold mapping approaches have been proposed [11, 16, 18] . The most important step towards RDB2RDF is two standard recommendations by the W3C RDB2RDF Working Group: Direct Mapping and R2RML mapping language [1, 12, 14] . With these remarkable achievements for RDB2RDF, it has been expected that the seamless integration of RDB data with RDF datasets towards the Web of Data can be easily actualized. In practice, encouraged by the standard mapping language R2RML, many RDB2RDF systems adopting this common language have been developed in various areas [11] . However, in spite of earnest efforts to adopt this common mapping language, the publication of RDB data on the Web in machine-readable RDF format did not yield the significant results as expected. Although direct mapping and R2RML seem to be inevitable for RDB2RDF, this approach has some limitations [11, 13] . The complex mapping structures of R2RML written in Turtle hinders its practical applications. Due to its robust structures, we cannot find fully-fledged R2RML processors. R2RML also does not address some common questions that occurred in RDB2RDF, such as the implementation of translation process and the way to access the mapped RDF datasets with SPARQL [19] . The translation of SPARQL queries into equivalent SQL queries is one of the crux issues in RDB2RDF [5, 8] . This paper proposes a noble and practical method based on schema translation for RDB2RDF. Since the conceptual schema of RDB is similar to ontological modeling of a certain domain, the mapping of RDB schema into RDF Schema is more effective than the conventional instance-based approaches. We describe how to resolve the intrinsic differences between RDB and RDF in schema level. In addition, we also show the ways to map the operational differences, such as graph pattern matching in RDF and JOIN operations in RDB. To extend this approach, we present a new schema-level mapping method and an effective way to implement SPARQL endpoint in RDB. The rest of the paper is organized as follows. Section 2 reviews related work on RDB-to-RDF mapping approaches, especially direct mapping method, and analyses the principles for the mapping approaches. Section 3 presents the detailed description of the proposed schemabase mapping: the underlying concepts, mapping method and mapping description. Section 4 deals with schema-based mapping system architecture and implementation of SPARQL endpoint in RDB with some typical mapping examples. Section 5 concludes the paper and discusses possible future work.
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