W3C RDB2RDF Incubator Group Report

1.1.1 Integrating Databases to Research Nicotine Dependency

Complex biological queries generally require the integration of information from several sources. To understand the genetic basis of nicotine dependence, gene and pathway information needed to be integrated and three complex biological queries answered using the integrated knowledge base. The gene information source NCBI Entrez Gene, which has gene-related records of ~2 million genes needed to be integrated with pathway information sources, such as KEGG (Kyoto Encyclopedia for Genes and Genomics). Comparing results across model organisms required homology information provided by the NCBI HomoloGene, containing homology data for several completely sequenced eukaryotic organisms).

An ontology-driven approach was used to integrate the two gene resources (Entrez Gene and HomoloGene) and the three pathway resources (KEGG, Reactome and BioCyc). An OWL ontology called the Entrez Knowledge Model (EKoM) was created for the gene resources and integrated with the extant BioPAX ontology designed for pathway resources. The integrated schema was populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created.

SPARQL was used to formulate queries to investigate the genetic basis of nicotine dependence over the integrated knowledge base:

• Which genes participate in a large number of pathways?
• Identify "hub genes" from the perspective of gene interaction?
• Which genes are expressed in the brain, in the context of neurobiology of nicotine dependence and various neurotransmitters in the central nervous system?

The result was very successful. The queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. See [NicotineDependence] for details.

1.1.2 Triplify: Exposing Relational Data on the Web

In order to make the Semantic Web useful to ordinary Web users, RDF and OWL have to be deployed on the Web on a much larger scale. Web applications such as Content Management Systems, online shops or community applications (e.g. Wikis, Blogs, Fora) already store their data in relational databases [triplify]. Providing a standardized way to map the relational data structures behind these Web applications into RDF, RDF-Schema and OWL will facilitate broad penetration and enrich the Web with RDF data and ontologies and facilitate novel semantic browsing and search applications.

By supporting the long tail of Web applications and thus counteracting the centralization of the Web 2.0 applications the planned RDB2RDF standardization will help to give control over data back to end-users and thus promote a democratization of the Web.

To support this usecase scenario, the mapping language should be easily implementable for lightweight Web applications and have a shallow learning curve to foster early adoption by Web developers.

1.1.3 Integration of Enterprise Information Systems

Efficient information and data exchange between application systems within and across enterprises is of paramount importance in the increasingly networked and IT-dominated business atmosphere. Existing Enterprise Information Systems such as CRM, CMS and ERP systems use Relational database backends for persistence. RDF and Linked Data can provide data exchange and integration interfaces for such application systems, which are easy to implement and use, especially in settings where a loose and flexible coupling of the systems is required.

Insight can often be gained by integrating data from databses built for different purposes in separate corporate silos. For example, integrating data from a bug database with a customer database may help understand ordering behavior as a function of the bugs encountered.

In Supply Chain Management (SCM), for example, it is vital to exchange product catalogs and other goods related information within a network of interconnected businesses involved in the ultimate provision of product and service packages. Such information is stored in relational databases and sometimes already exchanged electronically, but a variety of different technologies are used (e.g. proprietary files, XML files, DB dumps, Web Services etc.). Realizing a completely electronic information flow requires significant initial investments and currently limits the flexibility of businesses (e.g. with regard to changes in business partners). The envisioned RDB2RDF mapping language applied in conjunction with existing RDB based SCM systems will support the use of RDF and unique identifiers for realizing flexible information information flows accompanying supply chains.

The mapping language to be standardized by the proposed WG will simplify the publishing of enterprise data and information from Relational data backends and, thus, facilitate the interlinking and exchange of information between business information systems. In this scenario on-demand transformation of relational data to RDF, scalability and completeness with regard to the relational algebra are central requirements.

1.1.4 Ordnance Survey Use Case

Ordnance Survey, the National mapping agency of the UK, operates a very large geographical information system based on Oracle Spatial. The database contains topographical features, soil type and land use information. All these types of information are independently maintained and use separate terminologies. They describe the same land area but the boundaries of objects utilized for representing land use and soil type and topography do not coincide: For example, a pasture might consist of two distinct types of soil.

An example of a need to integrate this information is modeling filtration of pollutants into water bodies from agricultural land. The soil type determines the degree of filtration, the land use determines the type of pollutant. Topography determines whether the field is next to a water body.

An ontology exists for describing the types of objects in each database. The benefit from mapping the data to RDF is in simplifying querying and integration of the data. The very high volume of data makes an ETL approach impracticable, besides, the Oracle Spatial database offers spatial joining which is generally not available on RDF stores.

Thus, it is necessary to take SPARQL queries expressed in terms of the land use, soil type and topography ontologies and convert them into single SQL statements, with all joining and filtering to take place at the relational database. In the process, high level concepts need to be translated into SQL conditions on data that is not readily human readable.

Business questions to be answered by the use case are for example:

• What is the total length of river bank bordered by permeable soil used for grazing along a certain river?
• What types of crops are being cultivated within 100m of water, with total land use grouped by crop.
• What watter bodies are subject to high environmental load from agriculture, as defined by little current and extensive use of adjacent land.

From the viewpoint of RDB to RDF mapping, this usecase highlights the need to integrate data from different databases, built for different purposes. It also emphasizes need for extensibility in the mapping language for supporting RDBMS vendor specific features. In the present case, Oracle expresses a spatial join using a special type of derived table not found in standard SQL, thus the customization need is deeper than just supporting calls to native SQL functions.

The inference requirement consists primarily of expanding class membership into and's and or's of conditions on the relational data. In some cases, these conditions are spatial, such as bordering on or contained in. The user should be familiar with the ontologies but should not have to know about the classification codes used in the databases.