Abstract: Knowledge Graphs have rapidly become a mainstream technology that combines features of databases and AI. In this talk I will introduce Knowledge Graphs, explaining their features and the theory behind them. I will then consider some of the challenges inherent in both the theory and implementation of Knowledge Graphs and present some solutions that have made possible the development of popular language standards and robust and high-performance Knowledge Graph systems. Finally, I will illustrate the wide applicability of knowledge graph technology with example use cases including configuration management, fraud detection, semantic search & browse, and data wrangling.

Bio: Ian Horrocks is a full professor in the Oxford University Department of Computer Science, a visiting professor in the Department of Informatics at the University of Oslo and a co-founder of Oxford Semantic Technologies. He is also a Fellow of the Royal Society, a member of Academia Europaea, a fellow of the European Association for Artificial Intelligence (EurAI), a Fellow of the Alan Turing Institute and a British Computer Society Lovelace Medalist. His research concerns the representation of knowledge, and the efficient manipulation of such knowledge by computers. He played a leading role in establishing the Semantic Web as a significant research field, pioneering many of the underlying logics, algorithms, optimisation techniques, and reasoning systems. He has contributed to the development of several widely used reasoning systems including FaCT++, HermiT, Elk and RDFox. He has published more than 300 papers in major international conferences and journals, winning best paper prizes at KR-98, AAAI-2010, and IJCAI-2017, and test of time awards at ISWC-2013, KR-2020 and CADE-2021. He is one of the UK’s most highly cited computer scientists, with more than 57,000 citations, and an h-index of 99.

Abstract: The fight between the symbolic and sub-symbolic schools of AI appears to be mostly over, as there seems to be a growing consensus that AI needs the two approaches to join forces, lest we will face a new AI winter. In this talk, I will look at the fast growing field of Neuro-Symbolic AI from the point of view of rules: how neural networks are used to represent, learn and execute rules, what are (some of) the associated problems and challenges, what are the benefits from these approaches and why they are important for the future of AI. I will present solutions that have been proposed for different kinds of rules, and I will focus particularly on the case of decision rules.

Abstract: Answer Set Programming (ASP) has become a popular paradigm for declarative problem solving and is about to find its way into industry. This is due to its expressive yet easy knowledge representation language powered by highly performant (Boolean) solving technology. As with many other such paradigms before, the transition from academia to industry calls for more versatility. Hence, many real-world applications are not tackled by pure ASP but rather hybrid ASP. The corresponding ASP systems are usually augmented by foreign language constructs from which additional inferences can be drawn. Examples include linear equations or temporal formulas. For the design of "sound" systems, however, it is indispensable to provide semantic underpinnings right from the start. To this end, we will discuss the vital role of ASP's logical foundations, the logic of Here-and-There and its non-monotonic extension,Equilibrium Logic, in designing hybrid ASP systems and highlight some of the resulting industrial applications.

Bio: Torsten Schaub received his diploma and dissertation in informatics in 1990 and 1992, respectively, from the Technical University of Darmstadt, Germany, and his habilitation in informatics in 1995 from the University of Rennes I, France. From 1990 to 1993 he was a research assistant at the Technical University at Darmstadt. From 1993 to 1995, he was a research associate at IRISA/INRIA at Rennes. In 1995 he became University Professor at the University of Angers. Since 1997, he is University Professor for knowledge processing and information systems at the University of Potsdam. From 2014 to 2019, Torsten Schaub held an Inria International Chair at Inria Rennes - Bretagne Atlantique. Torsten Schaub has become a fellow of the European Association for Artificial Intelligence EurAI in 2012. From 2014 to 2019 he served as President of the Association of Logic Programming and was program (co-)chair of LPNMR'09, ICLP'10, and ECAI'14. The research interests of Torsten Schaub range from the theoretic foundations to the practical implementation of reasoning from incomplete, inconsistent, and evolving information. His particular research focus lies on Answer set programming and materializes at potassco.org, the home of the open source project Potassco bundling software for Answer Set Programming developed at the University of Potsdam.

Abstract: Application development continues to evolve, with many technologies addressing user needs and an ever increasing volume and complexity of use cases. This session looks at how the current trends of development democratization, task to process automation, and low-code are impacting the interest in, and adoption, of decision technologies, and extrapolates to their evolution in the late 2020s.