Recording – Transforming your Graph Analytics with GraphDB

graph analytics with GraphDB

This webinar has been recorded and available on demand.

Graph Analytics are a critical component in supporting business strategy. They can help you target content with pinpoint accuracy; identify potential new lifesaving treatments; flag possible fraudulent activities; and predict future behavior. GraphDB™ from Ontotext is accelerating the innovation of enterprise analytics to provide you with the strategic advantage your organization needs to get a competitive edge.

In this webinar Dr. Georgi Georgiev and Petar Ivanov, will discuss the foundations of graph analytics and the use of GraphDB™ to meet your analytic requirements.

Topics covered

  • What is the RDF graph data model and how it can be used for agile data integration and management
  • What is the SPARQL query language and how it can be used for querying and managing RDF data graphs
  • What are RDFS and OWL and how can they be used for semantic data modelling and creating ontologies and vocabularies for describing the graph data
  • Installing, configuring and fine tuning the GraphDB database and loading data into it
  • Working with the GraphDB Workbench – the graphical front-end for managing RDF graph data and databases
  • What are the various reasoning strategies, that RDF graph databases employ to derive new facts and enrich the knowledge graph
  • What are some distinguished performance optimizations of the GraphDB database
  • What are the various GraphDB extensions for full-text search, working with geo-spatial data, and performing fast analytics over large RDF graphs
  • Troubleshooting guide – what are the most common questions and their solutions

Webinar Duration: 120 minutes

Live Webinar: Yes

Live Question Answering: Yes

Resource After Webinar: Presentation and Recording

Georgi Georgiev

Dr. Georgi Georgiev has specialized in advanced text analytics. His focus is on machine learning based models and overall software architectures and methodologies for enterprise level semantic annotation and search solutions. Georgiev leads the text analysis product development at Ontotext and is the lead of semantic publishing projects for organizations such as the BBC and PressAssociation.


Petar Ivanov has a MS in Machine Learning from University College London and a MS in Neural Systems and Computation from the ETH & University of Zurich. At Ontotext he is part of the Innovation and Consulting Unit and works on training and consulting services related to semantic technology integration, graph databases, LOD, ontology modeling, vocabulary & thesaurus management, semantic publishing platforms, text analysis & semantic annotation.

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