Data Modeling and AI: From Semantic Networks to Knowledge Graphs
Maurizio Lenzerini, Sapienza University of Rome, Italy
Deep Reinforcement Learning to Improve Traditional Supervised Learning Methodologies
Luís Paulo Reis, Faculty of Engineering / LIACC, University of Porto, Portugal
Data Modeling and AI: From Semantic Networks to Knowledge Graphs
Maurizio Lenzerini
Sapienza University of Rome
Italy
http://www.diag.uniroma1.it/~lenzerini
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Brief Bio
Maurizio Lenzerini is a Professor of Data and Knowledge Management at the Department of Computer, Control, and Management Engineering of Sapienza University of Rome. His research interests lie at the intersection of Artificial Intelligence and Data Management, with emphasis on Knowledge Representation, Automated Reasoning, Knowledge Graphs, Ontology-based Data Access and Integration. He is the author of more than 300 publications on the above topics, and has delivered around 40 invited talks. According to Google Scholar he has an h-index of 82, and a total of 29534 citations (January 2023). He is a member of the Academia Europaea - The European Academy and the recipient of two IBM Faculty Awards, of the Peter Chen Award and of the ER (Entity-Relationship) Fellows Award. He is a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA), of EurAI (European Association for Artificial Intelligence), of the ACM (Association for Computing Machinery) and of AAAI (Association for the Advance of Artificial Intelligence).
Abstract
While data constitute one of the most important components of an AI system, the majority of research efforts today focus on ML models and algorithms, with the properties of data feeding such algorithms playing a secondary role. Thus, shifting the attention to data has been proposed as one of the most timely topics in AI research, under the name of Data-Centric AI. Arguably, the field of Knowledge Representation and Reasoning (KRR), and in particular its connection to the area of Data Modeling (DM), can provide important contributions towards shaping the research on Data-Centric AI. In this talk I will try to summarize the most important steps of the research done at the crossing between KKR and DM in the last decades, from the early work on Semantic Networks to the investigation on ontologies and Knowledge Graphs.
Deep Reinforcement Learning to Improve Traditional Supervised Learning Methodologies
Luís Paulo Reis
Faculty of Engineering / LIACC, University of Porto
Portugal
https://sigarra.up.pt/feup/en/func_geral.formview?p_codigo=211669
Brief Bio
Luis Paulo Reis is an Associate Professor at the University of Porto in Portugal and Director of LIACC – Artificial Intelligence and Computer Science Laboratory. He is an IEEE Senior Member, and he is the President of APPIA - Portuguese Association for Artificial Intelligence. He is also Co-Director of LIACD - First Degree in Artificial Intelligence and Data Science. During the last 25 years, he has lectured courses, at the University, on Artificial Intelligence, Intelligent Robotics, Multi-Agent Systems, and Simulation. He was the principal investigator of more than 30 research projects in those areas. He won more than 60 scientific awards including winning more than 15 RoboCup international competitions and best papers at conferences such as ICEIS, Robotica, IEEE ICARSC and ICAART. He supervised 22 PhD and 150 MSc theses to completion and is supervising 12 PhD theses. He was a plenary speaker at several international conferences, organised more than 60 international scientific events and belonged to the Program Committee of more than 250 scientific events. He is the author of more than 400 publications in international conferences and journals.
Abstract
This talk focuses on the intersection of Deep Reinforcement Learning (DRL) and traditional Supervised Learning (SL) methodologies, exploring how DRL can enhance performance and overcome challenges in tasks typically approached via SL. Despite the success of SL in various domains, its limitations, including the inability to handle sequential decision-making and non-stationary environments, are obvious, making DRL a potentially useful tool.
The talk will outline the fundamental principles of DRL, including its distinguishing features, such as learning from delayed rewards, handling the exploration-exploitation trade-off, and operating in complex, dynamic environments. It will also focus on the integration of DRL into traditionally SL-dominated areas, providing real-world examples from several fields. The talk will discuss how DRL can automate and optimise processes within the machine learning pipeline that have traditionally been manual and heuristic, such as hyperparameter tuning and feature engineering. By using DRL, the talk will showcase how these processes can be transformed into learnable tasks, improving the efficiency and performance of the supervised learning system. The talk will also present the latest research and techniques on the incorporation of DRL into traditionally SL-focused domains and feature interesting examples from several projects developed at the University of Porto on these areas of DRL and DRL for SL, such as the DRL methodologies included in our RoboCup world champion team in the humanoid 3D Simulation League.