Keynotes
“Conceptual Modeling and Knowledge Representation: a journey from Data Modeling to Knowledge Graphs”
Date TBA
Maurizio Lenzerini
Department of Computer, Control, and Management Engineering of Sapienza University of Rome.
Abstract
While data constitute one of the most important components of an information system, many research efforts today focus on Machine Learning models and algorithms, with the properties of data feeding such algorithms playing a secondary role. Thus, shifting the attention to data has been recently proposed as one of the most timely topics in Data Analytics and Artificial Intelligence (AI) research, under the name of Data-Centric AI. Arguably, the field of Conceptual Modeling (CM), and in particular its connection to the area of Knowledge Representation and Reasoning (KRR), 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 CM and KKR in the last decades, from the early work on Data Modeling and Semantic Networks to the investigation on ontologies and Knowledge Graphs.
Short Biography
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 Conceptual Modeling, 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 29806 citations (March 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).
Reverse Engineering of Language at Scale: Towards Symbolic and Explainable Large Language Models
Date TBA
Walid S. Saba
Senior Principal Scientist, Experiential Institute for Artificial Intelligence, Northeastern University
Abstract
Scientific explanation proceeds in one of two directions: by following a top-down strategy or a bottom-up strategy. For a top-down strategy to work, however, one must have access to a set of general principles to start with and this is certainly not the case when it comes to thought and how our minds externalize our thoughts in language. Lacking any general principles to start with, a bottom-up approach must be preferred in the process of discovering how language works. As such, we believe that the relative success of large language models (LLMs), that are essentially a bottom-up reverse engineering of language at scale, is not a reflection on the symbolic vs. subsymbolic debate but is a reflection on (appropriately), adopting a bottom-up strategy. However, due to their subsymbolic nature, LLMs are not really models of language, but statistical models of regularities found in language and thus whatever knowledge these models acquire about how language works will always be buried in billions of microfeatures (weights), none of which is meaningful on its own. Because they are incapable of maintaining the compositional structure of language, LLMs can never provide an explainable theory of how language works. To arrive at an explainable model of how language works, we argue in this talk that a bottom-up reverse engineering of language at scale must be done in a symbolic setting. Hints of how this should be done can be traced back to Frege, although it was subsequently and more explicitly argued for by Sommers (1963), Hobbs (1985) and Saba (2007).
Short Biography
Walid Saba is a Senior Research Scientist at the Institute for Experiential AI at Northeastern University. Prior to joining the institute in 2023, he worked at two Silicon Valley startups, focusing on conversational AI. This work included high-level roles as the principal AI scientist for telecommunications company Astound and CTO of software company Klangoo, where he helped develop its state-of-the-art digital content semantic engine (Magnet).
Saba’s career to date has seen him hold various positions in both the private sector and academia. His resume includes entities such as the American Institutes for Research, AT&T Bell Labs, IBM and Cognos, while he has also spent a cumulative seven years teaching computer science at the University of Ottawa, the New Jersey Institute of Technology (NJIT), the University of Windsor (a public research university in Ontario, Canada), and the American University of Beirut (AUB).
He has published over 45 technical articles, including an award-winning paper that he presented at the German Artificial Intelligence Conference (KI-2008). Walid received his BSc and MSc in Computer Science from the University of Windsor, and a Ph.D in Computer Science from Carleton University in 1999.