Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes

This webinar is recorded and available on YouTube.

The knowledge graph (KG) represents a shared conceptualization across the organization describing all-important business objects and concepts, their relations and metadata. Unlike traditional data warehouse systems, the KG organizes and unites diverse master data, reference data and metadata. Knowledge graphs are based on W3C standards, like RDF(S) and OWL, so they can guarantee a true application-independent and knowledge-centric approach with a self-describing schema. 

The formal semantics of RDFS and OWL allow enterprises to have strict definitions, allowing both human experts and machines to interpret unambiguously data collected from different sources. What is even more attractive, enterprises can augment their knowledge graphs by applying inference to uncover new information. The resulting KG will have more data than the sum of its constituent datasets. It is also better interconnected and allows faster discovery of multi-hop relationships and patterns. This way users get richer sets of relevant results in less time.

While automatic reasoning has always inspired the imagination, numerous projects have failed to deliver to the promises. The typical pitfalls related to ontologies and symbolic reasoning fall into two categories:

• Over-engineered ontologies. The selected ontology language and modeling patterns can be too expressive. This can make the results of inference hard to understand and verify, which in its turn makes KG hard to evolve and maintain. It can also impose performance penalties far greater than the benefits.
• Inappropriate reasoning support. There are many inference algorithms and implementation approaches, which work well with taxonomies and conceptual models of few thousands of concepts, but cannot cope with KG of millions of entities.
• Inappropriate data layer architecture. One such example is reasoning with virtual KG, which is often infeasible. 

The webinar will provide a brief introduction to logical reasoning and overview of the most popular semantic schema and ontology languages: RDFS and the profiles of OWL 2. We will demonstrate how reasoning works on FactForge – our public GraphDB demonstration service, exposing a knowledge graph of more than 2 billion statements, combining DBPedia, GeoNames, FIBO and other data. Next, we will introduce the different reasoning implementation approaches and discuss their fitness for specific application scenarios. Finally, we will provide a quick guidance for configuration and customization of reasoning with GraphDB.

In this webinar you will learn:

• Which OWL profiles are appropriate for reasoning with big knowledge graphs;
• What are the design choices for reasoning implementation, what are their pros and cons and how the popular triplestores implement reasoning;
• What are the basics for configuring reasoning with GraphDB.

Who is this webinar for:

• Technical people with basic knowledge of RDF(S) and OWL;
• Consultants and Solution Architects;
• Linked Data and Knowledge Graph Scientists.

Expected duration:

• 45 minutes presentation
• 15 minutes Q&A session