Live, online training tailored to meet one single goal – to help you deliver a successful Proof-of-Concept that uses Semantic Technology and a graph database for your business case. Teaching time is 9 + 1 hours including:

  • Pre-class assignments: 3 hours of video tutorials + 2 hours of practical assignments
  • Live class: 4 hours (two dates to choose from)
  • Individual consultation session: 1 hour


  • Wednesday, February 15 | 9:00 GMT – 13:00 GMT
  • Thursday, February 16 | 15:00 GMT – 19:00 GMT


  • €490
  • 20% Early bird discount if booked by February 1 –> €392
  • 30% Group discounts for a group of at least three participants –> €343

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PoC w/ GraphDB Training

What’s in this training for you?

Semantic Technology offers a broad range of advantages for data scientists, analysts and engineers, including: evolving schemas, flexible data modeling, automated knowledge discovery, access to colossal Linked Open Data resources, to name a few.

We will help you make your way through, by giving you:

    • a comprehensive overview of the semantic technology standards;
    • various design patterns and best practices in building data heavy systems;
    • introduction to GraphDB – a semantic graph database, compliant with the W3C Standards;
    • extensive practical hands-on experience on working with data;
    • the possibility to discuss your specific case.

Check out the Detailed Curriculum.


1-hour tête-à-tête individual consultaion!

Check Details

Join this training and get feedback on how to leverage SemTech tools to validate your specific use case.

Who is the training for?

Building a successful PoC requires a good understanding of the technology basics, as well as its practical implementation details. This training covers both aspects in a concise, yet comprehensive way. We will help:

    • Product managers,
    • System architects, and
    • Developers

to agree on their PoC functional design.

Discover the 5 important things that will make you succeed, book your seat!

Individual Consultation

The training is followed by individual 1-hour consultation, scheduled at the participant’s convenience, to turn the acquired knowledge into a practical solution to a specific business case. We all know that only applicable knowledge is power.

The session targets:

    • Review of the custom project goals;
    • Outline an implementation plan with quick achievements and possible challenges;
    • Define a strategy for success.

 Detailed Curriculum

Pre-class assignments (recorded sessions):

  • Semantic Technology Overview
  • Why Semantic Technology;
  • How to manage data as a graph;
  • Technology standards overview – RDF, RDFS;
  • Schema definition (Ontologies fundamentals, OWL, SKOS);
  • Querying data with SPARQL – query types, patterns and modifiers;
  • Why to use and publish Linked Data and Linked Open Data;
  • Success stories overview – AstraZeneca, FT, BBC, DSP, LinkedLifeData … ;
  • GraphDB overview – features, setup and configuration, examples;
  • 2 hours worth of tailored SPARQL exercises (+sample solutions) to get comfortable with designing and executing queries.

Live Class Sessions:

  • Designing PoC with GraphDB (1 hours);
  • Extract, Transform and Load (ETL) your data with GraphDB OntoRefine;
  • Merge and isolate data from multiple sources (using named graphs);
  • Find what you have – explore data in GraphDB;
  • Infer new data facts using basic ontologies;
  • Visualize data (SPARQL queries results);
  • Integrate new resources from LOD;
  • PoC design practical session (2.5 hours);
  • Following the same workflow above as a group we will clean, transform, enrich & link a rich (movies) dataset and explore how semantic technologies enable you to discover hidden insights about the data.
  • Lessons learned; Q&A (30 m).

We want to help you build a successful PoC that utilizes Semantic Technology.

Training Instructors

Georgi Georgiev

Georgi Georgiev, PhD. Head of the Text Analysis Unit and the Innovation and Consultancy Services

Georgi Georgiev is a leading expert in technical programming and technical architecture, linked data and cognitive technology, big data and text analytics.

He is an author of more than 60 insightful research and industry publications on today’s hot topics of machine learning, algorithms, linguistics, big data, analytics and semantic technology.


Petar Ivanov, MS in Machine Learning and Neural Systems & Computation

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.

His core expertise is semantic technology integration, graph databases, LOD, ontology modeling, and vocabulary & thesaurus management.

Ivelina Nikolova, Senior NLP Engineer

Ivelina Nikolova, PhD. Senior NLP Engineer

Ivelina Nikolova 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 state-of-the-art natural language processing techniques.

Vladimir Alexiev, Lead of Data and Ontology Modeling Team

Vladimir Alexiev, PhD. Lead of Data and Ontology Management Group

Vladimir’s experience includes ontology engineering, metadata standards, vocabularies and thesauri, RDF, RDFS, OWL2, SHACL, SKOS, SPARQL, LOD, mapping, R2RML, ETL, semantic web applications, project management, business analysis and requirements specifications. He has worked in various business domains, from Customs and Excise, to Personal Finance workflows, to Legal Procedures and Statistics, to Cultural Heritage and Digital Humanities.

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