Abstract: 

In our interconnected world, the broader context of data is crucial in influencing outcomes. However, current Business Analytics and decision-making practices often focus on isolated entities, neglecting the context. While Graph {Databases, Analytics, and Machine Learning} has been used for the last decade, only a few companies are able to leverage these Graph superpowers to their full extent. Large Language Models (LLMs), probably the most significant trend in the last year, are having a similar fate. Due to challenges such as hallucinations, internal knowledge access, and a lack of explanation of results, only a few companies can fully leverage the capabilities of LLMs.

Session Outline:

In this hands-on workshop, we will discover how the combination of Graph Data and LLMs can make each of them even more powerful. For this, we will cover the following topics:
 Graph Thinking, identifying, and modeling Graph problems 
 How to combine LLMs and Knowledge Graphs to: 
 Access custom data
 Minimize hallucination
 Provide explanations and traces of answers 
 Prompt engineering and fine-tuning to improve the LLM performance for specific use cases

Background Knowledge:

This workshop is hands-on, leveraging Jupyter notebooks, so there is no need to pre-install software and there are no prerequisites.

Bio: 

Anthony Mahanna joined the ML team at ArangoDB in July of 2023 as a Software Engineerto help build ArangoDB’s space in the world of GraphML and GraphLLMs. He discovered ArangoDB in 2021 while working on a personal project, which set him on the internship-to-full-time path at ArangoDB. Anthony holds a BSc Hons in Computer Science from the University of Ottawa, Canada.