At Center Stage III: Ontotext Webinars About GraphDB’s Data Virtualization Journey from Graphs to Tables and Back

A series of blog posts focusing on major Ontotext webinars and how they fit in the bigger picture of what we do. In this blog post, we want to present 3 webinars about data virtualization and how our RDF database for knowledge graphs GraphDB provides the tools for the full journey from knowledge graphs to relational tables and back.

September 24, 2021 6 mins. read Gergana Petkova

This post continues our series aiming to provide the bigger picture of what we do and how our webinars fit into it. All of our webinars are available on demand. If we’ve managed to pique your interest with this post, you can request a free recording.

So, we started this series by introducing knowledge graphs & their application in data management and how to reason with big knowledge graphs & use graph analytics.

Here, we want to talk about our flagship product GraphDB – an enterprise-ready RDF database optimized for the development and operations of knowledge graphs. More specifically, we want to focus on three webinars about data virtualization and how GraphDB provides the tools for the full journey from knowledge graphs to relational tables and back.

Although the RDF of knowledge graphs enables a schema-less approach to facilitate the integration of multiple heterogeneous datasets, the ecosystem of applications that rely on communication with relational databases is still large. Now, thanks to some of our latest releases, GraphDB allows those who need to work in SQL to access the power of their organization’s knowledge with SQL. It also makes it possible to retrieve information from external relational databases and create a virtual graph by mapping the columns and rows of a table to entities in the graph.

Now, let’s say a few words about each of our webinars.

From Strings to Things with the GraphDB 9.4 Mapping UI

In our first webinar From Strings to Things with the GraphDB 9.4 Mapping UI, our CTO Vassil Momtchev teaches you how to generate RDF data from various data formats. As we know that the task of generating good data models is a complex and error-prone process, we aim to lower this effort with the help of GraphDB. The webinar introduces important usability improvements and tools that allow the automation of extraction, transformation and loading (ETL) activities for building or updating knowledge graphs.

You will learn more about how to quickly transform various data formats like CSV, JSON or XML into RDF without writing code and guided by the currently loaded ontologies in GraphDB. Vassil will also show you how to clean and reconcile your data using the OntoRefine interface and, finally, how to batch and process large scale data using the GraphDB Mapping API.

Question: Are you planning to implement your own reconciliation service?

Ontotext Answer: Indeed, we are. It has been long in the making, and we are actively improving it at the moment. The Ontotext reconciliation service is in development and would be based on Elasticsearch indexing. The concept is that you create a connector to Elasticsearch and index the fields you want to use for reconciliation. Then, Elasticsearch queries are auto-generated for the cells you are trying to reconcile. The service will be fully compliant with the reconciliation specification.

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Hands-on With the JDBC Driver in GraphDB: Bridging Relational Queries to the Graph World

The second webinar in focus is Hands-on With the JDBC Driver in GraphDB: Bridging Relational Queries to the Graph World. It provides a brief overview of the GraphDB JDBC Driver and presents features such as SQL-to-SPARQL transformation and query optimization. Our speaker, Tomas Kovatchev, shows you how GraphDB makes it easier to consume unified information instead of having to rely on a handful of experts who have the full understanding of your organization’s ecosystem of knowledge and data. He also gives a quick demonstration of how to use the driver to access knowledge graphs in GraphDB from some of the most popular Business Intelligence tools (e.g., Power BI and Tableau), which require SQL access and cannot use SPARQL.

You will learn more about how to map SPARQL queries to SQL views and how GraphDB optimizes SQL queries by pushing complexity down to SPARQL. You will also see how to configure Tableau to consume data over the GraphDB JDBC driver as well as how to configure Microsoft Power BI to consume data over ODBC driver.

Interesting attendee question: What happens if a string-mapped value is an IRI? How is a string mapped to an IRI?

Ontotext answer: The JDBC driver does not differentiate between an IRI and a Literal, so you can have WHERE operations looking for both a Literal and an IRI. For example, if you have a SQL column mapped to String Literal values, you can have a WHERE clause such as WHERE gender=’male’ and if the column is mapped to IRIs you can have a clause: WHERE gender=’

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Data Virtualization with GraphDB

In the third webinar Data Virtualization with GraphDB, Vassil Momtchev demonstrates how GraphDB enables you to combine the two main principle approaches of integrating data: collect (ETL) vs connect (virtualization). With enterprise knowledge graphs like GraphDB, organizations can put their ecosystem of knowledge and data in context. In this way, they can provide unified access to and better interpretation of information across different data-centric systems and boost their productivity. The webinar focuses on the data virtualization features available by integrating the open-source Ontop platform and shows practical examples of how to use it.

You will learn more about data virtualization, its common use cases and main limitations. Vassil also explains the design principles of virtualization with GraphDB and how to write R2RML and OBDA file descriptors to map relational schema to graphs. He will cover simple and more advanced querying, indexing and reasoning with remote data as well as how to combine local and virtualized data via SPARQL federation.

Interesting attendee question: Is there any reasoner with GraphDB and Ontop?

Ontotext answer:  Ontop has built-in support for reasoning using RDFS and OWL 2 QL ontologies. The inference is performed at query time and thus may not be optimal for all use-cases. This is different from the forward-chaining reasoner embedded in GraphDB, which materializes the inferred statements when data is imported, and thus provides much better performance. Unfortunately, it isn’t possible to use GraphDB’s reasoner with Ontop as it requires fast access to all triples in order to compute the inferred statements.

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Wrap up

That covers the three webinars that we wanted to present to you today.

In a nutshell, we believe that you should be able to connect your data with your knowledge graph regardless of where that data lives on the internet or what format it happens to be in. With GraphDB’s data virtualization tools you open your graph to the wider semantic web and to relational databases. This creates amazing opportunities for applications to provide users with a single coherent view on the complex and diverse reality of your organization’s ecosystem of knowledge and data.

We hope you’ve heard enough to want to dive straight into one of them or why not all three?

Watch out for our next post in this series At Center Stage: Ontotext’s Webinars or visit our Webinars directly to check out what is upcoming!


Article's content

Content Manager at Ontotext

Gergana Petkova is a philologist and has more than 15 years of experience at Ontotext, working on technical documentation, Gold Standard corpus curation and preparing content about Semantic Technology and Ontotext's offerings.

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