GraphDB 9.4 now features SQL access to graphs over JDBC driver, visual interface for defining transformation of tabular data to RDF and mapping to existing graphs as well as RDF mapping API with streaming support for transforming tabular data into RDF
Ontotext has released the 9.4 version of GraphDB with the main objective to lower the cost of creating and consuming knowledge graphs. Although the RDF data model is a powerful data model for sharing meaning, the task of developing good models is still perceived as intricate and laborious work. In GraphDB 9.4 this is now simpler and less error-prone. It also allows knowledge graphs to be accessed over SQL, vastly broadening the number of tools (e.g., BI) that can consume the data and eliminate the need for specific integration.
Visual interface in OntoRefine to define transformation of tabular data to RDF and mapping to existing graphs
In GraphDB 9.4 the visual interface of OntoRefine is now optimized for guiding the user in choosing the right predicates and classes, defining the datatype to RDF mappings and implementing complex transformation using OpenRefine’s GREL language.
OntoRefine is an extended version of OpenRefine, which is a data wrangling tool optimized to clean tabular data and connect it with datasets like Wikidata, via reconciliation services. It also allows the transformation of any structured data to RDF and mapping it to a locally stored schema in GraphDB.
RDF mapping API with streaming support to transform tabular data
Once the user creates a mapping in OntoRefine, using the visual interface or directly SPARQL, an efficient mechanism is needed to script and automate the updates. The new GraphDB 9.4 Mapping API supports “data providers”, like an OpenRefine project or posted CSV data stream, and their automated transformation into RDF. This allows automation of extraction, transformation and loading (ETL) activities for building or updating knowledge graphs. The streaming API also guarantees no limitations on the size of the data.
SQL access to GraphDB over JDBC driver
GraphDB 9.4 adds important features to make it easier for business users and experts in big enterprises to consume unified information, addressing the challenges that only a handful of experts have a thorough understanding of the company’s ecosystem of knowledge and data and the tooling support may not be adequate.
GraphDB now supports accessing RDF models with any JDBC/ODBC compatible tool. The knowledge experts can define SQL views over any SPARQL query results. GraphDB supports full SQL language, via Apache Calcite, and some of the constructs will be pushed down to the SPARQL query for maximum efficiency.
An immediate benefit of the JDBC driver is that knowledge graphs managed in GraphDB can be accessed from some of the most popular Business Intelligence tools (e.g. Power BI and Tableau) and ETL software packages require SQL access and cannot use SPARQL. Such integration will be demonstrated during the September 17th webinar “Hands-on with the JDBC Driver in GraphDB: Bridging relational queries to the graph world“.
Beyond GraphDB 9.4
GraphDB 9.4 is an important release because it lowers substantially the cost of integrating and consuming data. Our ambition is growing bigger with 9.5, adding No-ETL integration and data virtualization with relational-to-graph mapping (R2ML) and ontology-based data access (OBDA). By enabling the users to expose Oracle, PostgreSQL, IBM DB2, MySQL databases as virtual SPARQL endpoints, we will enable: (1) The easy synchronization of relational data with GraphDB trough loading the content of the endpoint; (2) The expansion of the query federation capabilities with data joins between RDF and relational databases.