Keynote Speaker Ⅰ
Prof. Haofen Wang
Tongji University, China
Co-founders of OpenKG
A short introduction to Prof. Haofen Wang:
Haofen Wang is a professor at College of Design & Innovation, Tongji University. Prior to that, he served as CTOs for two well-known AI startups (i.e., Leyan and Gowild). He is also one of the co-founders of OpenKG, the world-largest Chinese open knowledge graph community. He has taken charge of several national AI projects and published more than 100 related papers on top-tier conferences and journals. He developed the first interactive emotional virtual idol in the world. The intelligent assistant he built has answered questions from more than one billion users when they did online shopping. He has also served as deputy directors or chairs for several NGOs like CCF, CIPS and SCS.
Speech Title: Towards Intelligent System Driven by Knowledge Graph Enhanced Large Language Model
Abstract: With the rise of large language models (LLMs) led by ChatGPT, their powerful understanding ability, incredible generation and even multi-turn dialogue capabilities have attracted attention in the field of AI, especially in natural language processing (NLP) and knowledge graph (KG). The general intelligence of LLMs for tasks will inevitably replace previous work that focused on specific data or tasks, while more challenging tasks will emerge. In this report, we will focus on the LLM era, discussing whether knowledge graphs are necessary, what kind of knowledge graphs are needed, how to use LLMs to build and manage better knowledge graphs, how to build better knowledge graph fusion with LLMs, and the collaborative and fusion paradigms of the two. We will explore the opportunities, challenges, and solutions for knowledge graph research and development in the LLM era, and refine theoretical innovations and new research and development paradigms.
Keynote Speaker Ⅱ
Prof. Huiyu Zhou
University of Leicester, UK
A short introduction to Prof. Huiyu Zhou:
Dr. Huiyu Zhou received a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou currently is a full Professor at School of Computing and Mathematical Sciences, University of Leicester, United Kingdom. He has published over 380 peer-reviewed papers in the field. He was the recipient of "CVIU 2012 Most Cited Paper Award", “MIUA 2020 Best Paper Award”, “ICPRAM 2016 Best Paper Award” and was nominated for “ICPRAM 2017 Best Student Paper Award” and "MBEC 2006 Nightingale Prize". His research work has been or is being supported by UK EPSRC, ESRC, AHRC, MRC, EU, Royal Society, Leverhulme Trust, Invest NI, Puffin Trust, Alzheimer’s Research UK, Invest NI and industry. Homepage: https://www2.le.ac.uk/departments/informatics/people/huiyu-zhou.
Speech Title: Learning and modelling in image analysis
Abstract: There are many questions to answer in image interpretation and understanding. Uncertainty in image analysis needs strong and powerful modelling tools to describe the objects in the images. Artificial intelligence (AI) plays a very important role in the design of a robust tool for image representation. Using some examples from his own work on uncertainty analysis, Prof. Zhou will explore how AI can stimulate new concepts or development of dealing with complicated problems and lead us to novel adventures through these applications.
Keynote Speaker Ⅲ
Prof. Yulan He
Kings College London, UK
A short introduction to Prof. Yulan He:
Yulan He is a Professor at the Department of Informatics in King’s College London. She is currently holding a prestigious 5-year UKRI Turing AI Fellowship. Yulan’s research interests lie in the integration of machine learning and natural language processing for text analytics. She has published over 200 papers on topics including natural language understanding, sentiment analysis, topic and event extraction, question-answering, fake news detection, biomedical text mining, and social media analytics. She has received several prizes and awards, including a SWSA Ten-Year Award, a CIKM 2020 Test-of-Time Award, and AI 2020 Most Influential Scholar Honourable Mention. She has served as the General Chair for AACL-IJCNLP 2022 and a Program Co-Chair for EMNLP 2020. Yulan obtained her PhD degree in Spoken Language Understanding from the University of Cambridge.
Speech Title: Interpretable Language Understanding
Abstract: Large-scale Language Models (LLMs) such as ChatGPT, GPT-4 and LLaMA, have exhibited significant progress in natural language understanding. However, their opaque nature makes comprehending their inner workings and decision-making processes challenging for humans. In this talk, I will share the research undertaken in my group to address the interpretability concerns surrounding neural models in language understanding. This includes a hierarchical interpretable text classifier going beyond word-level interpretations, uncertainty interpretation of text classifiers built on LLMs, and explainable recommender systems by harnessing information across diverse modalities. I will conclude my talk by offering insights into potential future developments in interpretable language understanding.
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