Recording – Transforming your Graph Analytics with GraphDB 8.1

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 behaviour. 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 Ivelina Nikolova, PhD will discuss the foundations of graph analytics and the use of GraphDB™, as well as the newest features in version 8.1. One of them is the improved user experience that includes a brand new tool for visually exploring complex data models.

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: 60 minutes

Live Webinar: Yes

Live Question Answering: Yes

Resource After Webinar: Presentation and Recording

Ivelina Nikolova, Senior NLP Enigneer

Ivelina Nikolova – Senior NLP Engineer

Ivelina has experience in various projects for semantic textual enrichment and document classification. Her main expertise is in the area of information extraction such as named entity recognition, event recognition and relation extraction with hybrid methods combining rule-based and machine learning techniques.

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