View source for Bringing Relational Databases into the Semantic Web: A Survey

Relational databases are considered one of the most popular storage solutions for various kinds of data and they have been recognized as a key factor in generating huge amounts of data for Semantic Web applications. Ontologies, on the other hand, are one of the key concepts and main vehicle of knowledge in the Semantic Web research area. The problem of bridging the gap between relational databases and ontologies has attracted the interest of the Semantic Web community, even from the early years of its existence and is commonly referred to as the database-to-ontology mapping problem. However, this term has been used interchangeably for referring to two distinct problems: namely, the creation of an ontology from an existing database instance and the discovery of mappings between an existing database instance and an existing ontology. In this paper, we clearly define these two problems and present the motivation, benefits, challenges and solutions for each one of them. We attempt to gather the most notable approaches proposed so far in the literature, present them concisely in tabular format and group them under a classification scheme. We finally explore the perspectives and future research steps for a seamless and meaningful integration of databases into the Semantic Web.

We close this paper by mentioning some problems that have only been lightly touched upon

by database to ontology mapping solutions as well as some aspects that need to be considered by future approaches. 1. Ontology-based data update. A lot of approaches mentioned offer SPARQL based access to the contents of the database. However, this access is unidirectional. Since the emergence of SPARQL Update that allows update operations on an RDF graph, the idea of issuing SPARQL Update requests that will be transformed to appropriate SQL statements and executed on the underlying relational database has become more and more popular. Some early work has already appeared in the OntoAccess prototype and the extensions on the D2RQ tool, D2RQ/Update 21 and D2RQ++. However, as SPARQL Update is still under devel- opment and its semantics is not yet well defined, there is some ambiguity regarding the transformation of some SPARQL Update statements. Moreover, only basic (relation-to-class and attribute-to-property) mappings have been investigated so far. The issue of updating relational data through SPARQL Update is similar to the classic database view update problem, therefore porting already proposed solutions would contribute significantly in dealing with this issue. 2. Mapping update. Database schemas and ontologies constantly evolve to suit the changing application and user needs. Therefore, established mappings between the two should also evolve, instead of being redefined or rediscovered from scratch. This issue is closely related to the previous one, since modifications in either participating model do not simply incur adaptations to the mapping but also cause some necessary changes to the other model as well. So far, only few solutions have been proposed for the case of the unidirectional propagation of database schema changes to a generated ontology and the consequent adaptation of the mapping. The inverse direction, i.e. modification of the database as a result of changes in the ontology has not been investigated thoroughly yet. On a practical note, both database trigger functions and mechanisms like the Link Maintenance Protocol (WOD-LMP) from the Silk framework could prove useful for solutions to this issue. 3. Generation of Linked Data. A fair number of approaches support vocabulary reuse, a factor that has always been important for the progress of the Semantic Web, while a few other approaches try to discover automatically the most suitable classes or properties from popular vocabularies that can be mapped to a given database schema. Nonetheless, these efforts are still not adequate for the generation of RDF graphs that can be smoothly integrated in the Linking Open Data (LOD) Cloud. For the generation of true Linked Data, the real world entities that database values represent should be identified and links between them should be established, in contrast with the majority of current methods, which translate database values to RDF literals. Lately, a few interesting tools that handle the transformation of spreadsheets to Linked RDF data by analyzing the content of spreadsheet tables have been presented, with the most notable examples being the RDF extension for Google Refine and T2LD. Techniques as the ones applied in these tools can certainly be adapted to the relational database paradigm. These aspects, together with the challenges enumerated in Section 7, mark the next steps for database to ontology mapping approaches. Although a lot of ground has been covered during the last decade, it looks like there is definitely some interesting road ahead in order to seamlessly integrate relational databases

with the Semantic Web, turning it into reality at last.