Knowledge Graphs and Large Language Models

This 2-day online course is designed to equip participants with a thorough comprehension of the synergistic potential between knowledge graphs and LLMs. Throughout the program, participants will explore fundamental steps for initiating and executing a knowledge graph development project, receiving practical guidance on the effective utilisation of LLMs at different stages of the process. The course will also focus on empowering participants to strategically employ knowledge graphs to augment the accuracy, reliability, and explainability of Large Language Models.

Event Details
  • Days
    Hours
    Min
    Sec
  • Start Date
    February 24, 2025 9:00 am
  • End Date
    February 25, 2025 5:00 pm
  • Location
  • Price
      £995 + Vat (vat only charged if UK resident)
Course Instructor
Founder and Principal Educator
OWLTECH 
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. Born and raised in Athens, Greece, Panos currently works as a principal educator & consultant at OWLTECH, developing and delivering training workshops that provide actionable knowledge and insights for data and AI practitioners. He also works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain.  Panos has published several papers at international conferences, journals and books, and he is a regular speaker in both academic and industry venues, striving to bridge the gap between academia and industry so that they can benefit from each other. He is also the author of the O’Reilly book “Semantic Modeling for Data – Avoiding Pitfalls and Dilemmas”, a practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world.
Who is this Course for?

Data practitioners: Aspiring or practicing data scientists, data engineers or data analysts, seeking to deepen their understanding of knowledge graphs and LLMs, their implementation, and the technical intricacies involved.
Technology Leaders: Architects, CTOs , and IT professionals exploring or leading initiatives involving data integration, semantic technologies, and generative AI.

Why take this Course?

By the end of this live online 2-day course, you’ll be able to:
 

  • 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 organisation, 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
About this Course

In today's data-driven landscape, the concept of a knowledge graph has emerged as a pivotal framework for managing and utilising interconnected data and information. Stemming from Google's proclamation that shifted the focus from searching for strings to understanding entities and relationships, the term encapsulates a network of interconnected entities and concepts that facilitates semantic data integration, sharing, and utilisation within organisations.

At the same time, the advent of Large Language Models (LLMs) has revolutionised the field of natural language processing and artificial intelligence. These models, exemplified by architectures like GPT-4, have the capability to comprehend and generate human-like text on an unprecedented scale. By leveraging vast amounts of pre-existing linguistic data, LLMs excel in tasks such as language translation, text completion, and even creative content generation.

The synergy between knowledge graphs and LLMs represents a powerful combination that can significantly enhance various stages of knowledge graph development, such as schema design, knowledge acquisition and quality control, while eliminating LLMs hallucinations and improving their accuracy, reliability, and explainability.

This 2-day online course is designed to equip participants with a thorough comprehension of the synergistic potential between knowledge graphs and LLMs. Throughout the program, participants will explore fundamental steps for initiating and executing a knowledge graph development project, receiving practical guidance on the effective utilisation of LLMs at different stages of the process. The course will also focus on empowering participants to strategically employ knowledge graphs to augment the accuracy, reliability, and explainability of Large Language Models.

Will I gain a Certification/Accreditation?

Upon successful completion of this course, you will receive a certificate of completion from IRM UK.

Full Course Outline

The course consists of 8 modules:

Module 1 – Understanding knowledge graphs and their relation with LLMs

  • Clarification of the knowledge graph concept
  • What are knowledge graphs and why would you want to build one
  • 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

Module 2 – Crafting a knowledge graph strategy

  • Exploring 5 key questions that are essential to answer before initiating knowledge graph development
  • How to answer these questions and craft a development strategy

Module 3 – Specifying the knowledge graph:

  • Typical knowledge graph specification
  • Use of competency questions for gap analysis between organisational knowledge capabilities and needs
  • Scoping and prioritising knowledge graph features and content

Module 4 – Designing the knowledge graph schema

  • Building elements of a knowledge graph
  • Knowledge graph schema design using Semantic Web languages (RDF, OWL, SKOS, SHACL) and Labeled Property Graphs.
  • Conceptual modeling best practices, dilemmas, and pitfalls
  • Addressing uncertainty and vagueness
  • Using LLMs in schema design: what works and what doesn’t

Module 5 – Populating the knowledge graph

  • Understanding and defining knowledge graph population tasks such as entity and relation extraction
  • 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

Module 6 – Managing knowledge graph quality:

  • Knowledge graph quality dimensions, metrics, and trade-offs.
  • Typical quality problems and debugging methods
  • Detecting and correct quality problems in knowledge graphs 
  • Crafting a quality management strategy

Module 7 – Applying knowledge graphs:

  • Typical knowledge graph applications
  • Developing analytics solutions with knowledge graphs
  • Using knowledge graphs to ground Large Language Models

Module 8 – Maintaining and evolving knowledge graphs:

 

  • Addressing the challenge of knowledge graph maintenance and evolution
  • Detecting, measuring, and monitoring concept drift
  • Crafting a knowledge graph governance strategy.
Event Details
  • Days
    Hours
    Min
    Sec
  • Start Date
    February 24, 2025 9:00 am
  • End Date
    February 25, 2025 5:00 pm
  • Location
  • Price
      £995 + Vat (vat only charged if UK resident)
Training Brochure