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 and Data Warehouse Professionals: 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.