¹Zhejiang University,  ²The University of Manchester,  ³University of Glasgow

Full slide deck:  LoG-Tutorial-Part-123-V1-24Nov.pdf

Tuesday, 26th November, 2024

In recent years, Knowledge Graphs (KG) and Large Language Models (LLMs) have emerged as powerful tools for scientific research and knowledge discovery. This tutorial aims to provide attendees with an understanding of how these technologies can be integrated and applied to advance research in life sciences and other scientific domains. Over three hours, participants will explore the foundational concepts of KGs and LLMs, their applications in life sciences, and practical examples demonstrating their integration for BioNLP tasks. The tutorial will include materials demonstrating KG construction and LLM development for scientific data interpretation, and emphasizing practical techniques for incorporating domain-specific knowledge into AI models. By the end of this tutorial, participants will have a comprehensive understanding of the synergy between KGs and LLMs and how to leverage these tools for innovative scientific solutions.

Length

10 mins

15 mins

15 mins

20 mins

10 mins

10 mins

30 mins

10 mins

10 mins

10 mins

15 mins

15 mins

10 mins

Content

Part I – Introduction

Introduction to KGs and LLMs

Part II – Knowledge Graphs for Science 

KG Definitions and Core Concepts

Application of KGs in Life Sciences: A Brief Review

A Case Study on Ecotoxicological Effect Prediction

Break

Part III – Large Language Models for Science

Introduction to Scientific LLMs

Review of Scientific LLMs: Textual, Protein, and Multimodal

Scientific Agents and Co-Pilots

Break

Part IV – Integrating KGs and LLMs for Scientific Applications

Knowledge Incorporation Frameworks Using LLMs

KG Integration for BioNLP Tasks

KG Integration for Scientific Prediction Tasks

Summary and Q&A

Speaker

Emine Yilmaz

Jiaoyan Chen

Jiaoyan Chen

Jiaoyan Chen

Qiang Zhang

Qiang Zhang

Qiang Zhang

Zaiqiao Meng

Zaiqiao Meng

Zaiqiao Meng

Qiang Zhang