Transforming Data to Intelligence with Knowledge Graphs and Large Language Models

Empower Your Data Modeling and Analytics with AI-Driven Contextual Intelligence

 

In today’s data-driven world, the ability to extract meaningful insights from interconnected information is critical. Over the past decade, knowledge graphs have emerged as the foundational framework for managing complex networks of data, helping organizations connect entities and concepts to unlock deeper insights. For data modelers, BI analysts, and data scientists, knowledge graphs provide a structured approach to semantic data integration and enable better decision-making through connected data.

At the same time, Large Language Models (LLMs) like OpenAI’s GPT have revolutionized natural language processing and AI-powered analytics. These models excel in understanding and generating human-like text, automating tasks such as language translation, text summarization, and even semantic search. As such, for data scientists and analysts, LLMs can be a powerful tool for advanced analytics, enabling better insights through their deep understanding of context and language.

When combined, knowledge graphs and LLMs can create a synergistic effect that improves the way data is modeled, analyzed, and utilized. This course will show you how to leverage knowledge graphs to enhance the accuracy, reliability, and explainability of LLM outputs, while also showing how LLMs can improve the schema design, knowledge acquisition, and quality control aspects of knowledge graphs.

This 2-day course is designed to equip data professionals with the knowledge and practical skills needed to integrate Knowledge Graphs and Large Language Models (LLMs) into their data modeling and analytics workflows. Combining theory with hands-on practice, students will learn every essential step for launching and managing a knowledge graph development project, with practical guidance on leveraging LLMs effectively at each stage. They will also learn how to combine knowledge graphs within LLM-based applications to enhance the latter’s performance and reliability.

Technologies and Tools

In this course, we will explore a range of cutting-edge technologies and tools essential for working with knowledge graphs and large language models. For knowledge graphs, we will focus on industry-standard frameworks such as RDF/OWL and querying with SPARQL, alongside practical tools like Protege for ontology development and GraphDB for graph storage and querying. Participants will also gain hands-on experience with Neo4j and Cypher. On the LLM side, we’ll be leveraging OpenAI’s GPT models, exploring how they can enhance the accuracy and usability of knowledge graphs. Finally, we will introduce Langchain, a robust framework designed to simplify the integration of language models with external data sources, making it easier to orchestrate complex workflows and automate processes.

 

Learning Objectives

  • Decide whether a knowledge graph is a proper solution for your data challenges, and specify its desired characteristics.
  • Understand the key factors determining the feasibility and viability of implementing a knowledge graph in an organization, and craft a proper development strategy
  • Design a knowledge graph’s schema in a way that makes the rest of its development much easier
  • Apply state-of-the-art tools and methods to (semi-)automatically populate a knowledge graph from diverse data sources.
  • Implement mechanisms to assess and improve the quality of a knowledge graph
  • Design and implement a knowledge graph evolution and governance strategy.
  • Use LLMs to enhance all the steps of developing a knowledge graph
  • Use knowledge graphs to ground LLMs and make them more accurate and reliable.

 

Who is it for?

  • Data Modelers: Professionals focused on designing and building data structures who want to leverage cutting-edge AI for more dynamic and context-aware data modeling.
  • Business Intelligence Analysts: Analysts seeking to enhance their ability to interpret and complex data by incorporating semantic relationships and AI-driven insights.
  • Data Scientists: Practitioners looking to integrate knowledge graphs and language models into their workflows to improve predictive analytics and decision-making.
  • Enterprise Data Professionals: Those working in enterprise environments who want to stay ahead by integrating the latest AI tools to solve complex organizational challenges.
  • Technology Leaders: Architects, CTOs , and IT professionals exploring or leading initiatives involving data integration, semantic technologies, and generative AI.

 

Panos Alexopoulos

Founder
Panagiotis Alexopoulos
Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. He currently works as Head of Ontology at Textkernel BV, in Amsterdam, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain.

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Van der Valk Hotel Utrecht
Winthontlaan 4-6
3526 KV Utrecht
Telefoon 030 8000 800

The hotel is very well accessible by public transport. From busstop ‘Kanaleneiland Zuid’ it is only a three-minute walk. You can take buses 63, 65, 66, 74 and 77 from Utrecht Central Station and you also take the tram line 20 or 21 from the train station and get off at stop ‘Kanaleneiland’. Please consult www.9292.nl (door-to-door journey planner, also available in English) or call 0900-9292 (travel advice by phone, € 0.70 p/m).

Van der Valk Hotel Utrecht is also located next to the highway A12, exit 17 (Utrecht / Jaarbeurs / Kanaleneiland).

Although the hotel has a large parking garage, we cannot guarantee parking spots. We therefore advise you to go by public transport.

For those who would like to arrive the day before, there is the possibility of staying at the Van der Valk Hotel Utrecht. However, the hotel does not provide special discounts for attendees of events. Therefore, when interested in an overnight stay, please consult Van der Valk directly to make a reservation.

More information about the hotel and the location can be found on their website www.vandervalkhotelutrecht.nl.

The course starts at 09.30 am and ends at 5 pm. Registration commences at 08.30 am.

 

The course consists of 5 modules:

Module 1: Understanding knowledge graphs and their relation with large language models

  • What are knowledge graphs and why we build them
  • Key factors influencing the ease or difficulty of building a knowledge graph
  • Large Language Models (LLMs) and their interplay with knowledge graphs
  • Knowledge graph development lifecycle
  • How to craft a knowledge graph development strategy
  • Group Activity: Explore 5 key questions that are essential to answer before initiating knowledge graph development

 

Module 2 – Designing the knowledge graph schema

  • Building elements of a knowledge graph
  • Eliciting schema requirements through competency questions
  • Group Exercise: Derive knowledge graph competency questions for a business process scenario
  • Knowledge graph schema design using Semantic Web languages (RDF, OWL) and Labeled Property Graphs (Neo4j).
  • Conceptual modeling best practices, dilemmas, and pitfalls
  • Using LLMs in schema design: what works and what doesn’t
  • Group Exercise: Design a knowledge graph schema for a given set of competency questions

 

Module 3: Populating the knowledge graph

  • Understanding and defining knowledge graph population tasks
  • Evaluating and selecting data sources and population tools
  • Designing population strategies and pipelines.
  • LLMs as knowledge providers and knowledge miners – what works and what doesn’t
  • Hands-on exercise: Mining entity relations from texts with LLMs

 

Module 4: Applying knowledge graphs

  • Typical knowledge graph applications
  • Entity linking and disambiguation
  • Using knowledge graphs to ground Large Language Models
  • Hands-on exercise: Building a question answering system over knowledge graphs

 

Module 5 – Managing knowledge graph quality

  • Knowledge graph quality dimensions, metrics, and trade-offs.
  • Typical quality problems and debugging methods
  • Detecting and correcting quality problems in knowledge graphs
  • Hands-on exercise: Automatically debugging a knowledge graph
  • Addressing dilemmas of knowledge graph maintenance and evolution
  • Crafting a knowledge graph evolution strategy.

 

Taking part in this hands-on workshop will only cost € 1305 when registering 30 days beforehand and € 1450 per person after the Early Bird period expires (excl. 21% Dutch VAT). This also covers documentation, coffee and lunch.

In completing your registration form you declare that you agree with our Terms and Conditions.

*) All pricing is VAT-excluded and EU VAT regulation stipulates that if you attend an event on-premise in The Netherlands, we are required to include local VAT. In case of discrepancy in registration fee between the website and the PDF brochure, the information on this page of the website always prevails.

Extra discounts
Discounts are available for group bookings of two or more delegates representing the same organization made at the same time. Ten percent off for the second and third delegate and fifteen percent off for all delegates when registering four or more delegates (all delegates must be listed on the same invoice).
This cannot be used in conjunction with other discounts.

Payment
Full payment is due prior to the event. An invoice will be sent to you containing our full bank details including BIC and IBAN. Your payment should always include the invoice number as well as the name of your company and the delegate name.
Payment by credit card is also available. Please mention this in the Comment-field upon registration and find further instructions for credit card payment on our customer service page.

In-house Info

Practically all of our seminars and workshops can be offered as an In-house course for your company exclusively. We can tailor with extra focus on specific topics that apply to your organization. Also available in online format or in face-to-face format with live video stream.

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