On 5th and 8th February 2024, CIRES CI Prof Amir Aryani, alongside Prof. Sören Auer from TIB, hosted two workshops focusing on Knowledge Graphs, AI, and Research Data. These events were held in Melbourne and Canberra, marking a significant part of the Leibniz Association delegates’ visit to Australia. Attendees were treated to a series of engaging presentations on the innovative use of Artificial Intelligence. Special thanks to the workshop speakers:

• Prof. Sören Auer (TIB) for his insights on “Towards Neuro-Symbolic AI with Large Language Models and Knowledge Graphs” and “Organising Scientific Knowledge with the Open Research Knowledge Graph”.
• Prof. Geoff Webb (Monash University) on “Large Language Models for Scientific Discovery.”
• Assoc. Prof. Yuan-Fang Li (Monash University) for discussing “Bridging the Language-Knowledge Divide.”
• Prof. Gholamreza (Reza) Haffari (Monash University) on “Certifying the Knowledge and Reasoning of LLMs using Knowledge Graphs.”
• Assoc. Prof. Gianluca Demartini (University of Queensland) for his work on “Managing Bias in Data Pipelines.”
• Dr. Bernardo P. Nunes (ANU) on “Data, AI, and Education.”
• Dr. Sergio José Rodríguez Méndez for introducing “FutureSOILS.”
• Dr. Jingbo Wang (NCI) for sharing the “NCI AI Development Effort.”
• Assoc. Prof. Amir Aryani (Swinburne University) on the “National Persistent Identifier Graph.”

Co-hosted by Swinburne and Leibniz Institute
Organizers: Sören Ayer (TIB Leibniz Information Center for Science and Technology & Leibniz University of Hannover) & Amir Aryani (Swinburne University)

Date and Location:
Monday, February 5: Learning and Teaching Building, Monash University, Clayton, Victoria
Thursday, February 8: Australian National University, Canberra

Workshop summary
The advent of Transformer technology and the rapid advancement of generative AI have significantly influenced research and scientific discovery. These technologies facilitate the analysis and synthesis of extensive data volumes, revolutionising traditional methods. Knowledge graphs have become a cornerstone in this evolution, providing a structured framework to encapsulate the intricacies of scientific concepts and findings.

This workshop explores the synergistic integration of AI and knowledge graphs in various data-intensive scientific fields. Our goal is to foster the development of innovative approaches in AI, knowledge graphs, and advanced data analytics to enhance research data utilisation. Our focus is to enable an interdisciplinary approach to leverage Knowledge Graph and AI to support better use and reuse of research data.

We envision this workshop as a collaborative platform, offering insights and strategies to transform individual research endeavours into collaborative and innovative interdisciplinary research projects. The target audience is researchers engaged in data-driven projects (e.g. health, environment), research managers, and research support teams who enable data infrastructures such as high-performance computing.