Declarative AI 2020

Abstract: As the real world opens up to the possibilities of machine learning and the use of analytics to inform operational decision making, the emphasis changes from development of fundamentally new approaches and methods, to driving widespread adoption. This session will span the spectrum of data science, machine learning, analytics and decision management as we explore what Gartner calls “Enterprise AI” – better automation, access and acceleration efforts are the keys to success. Another theme we will touch on is the need for technologists and product developers to focus on solving the real business problems and pain points of business rather than assuming a horizontal capability will somehow deliver long-term success.

Derek Miers is a Sr Director Analyst at Gartner and has been an industry analyst and consultant for more than 25 years, focused on the areas of digital transformation, business architecture, process management and technology innovation. He has been advising major organizations across industry and government. Prior to Gartner, Mr. Miers worked in research and consulting companies including Forrester Research where he published over 60 reports on business architecture, digital transformation and business process management. He covered business and enterprise architecture, digital transformation and business process management. He has been advising major organizations across industry and government. His deep competence is around process modeling/automation and enterprise architecture, especially as they relate to setting up, scaling and running major digital transformation initiatives. He focuses on how the use of models drive adaptability and agility; how they inform, connect and translate technology strategy into digital transformation initiatives; and then on into operational execution and analytics. He has developed a range of co-creation techniques for driving engagement in change programs and scaling transformation efforts. His primary focus is on establishing governance around change programs, innovation and transformation initiatives, linking that back to organizational and corporate/business strategy. That also entails establishing Centers of Expertise (CoEs) and other vehicles for scaling the engagement of the business and the adoption of technology. He also covers business process architecture and the fusion of design thinking through into operational excellence. That process architecture also includes the industrialization/harmonization of processes across federated enterprises, as well as the methods and techniques associated with knowledge workers and their processes.

Abstract: Smart speakers such as Alexa and later Google Home introduced Artificial Intelligence (AI) in billions of households making AI an everyday experience. We can now look for information, order products and services, without leaving the house or touch a computer. We just talk to a box and this thing will friendly perform the desired tasks for us. These new communication channels define a new challenge for successful eMarketing and eCommerce. Just running a traditional web site with many colorful pictures is no longer state of the art. Actually the web is currently reinventing itself by applying schema.org. Data, content, and services (i.e., resources) become semantically annotated allowing software agents, so-called bots, to search through the web understanding its content. The user nowadays consult their personal bot to find, aggregate, and personalize information, to reserve, book, or buy products and services. In consequence, it becomes increasingly important for providers of information, products and services to be highly hearable and visible in these new online channels to ensure their future economic maturity.

In our talk, we discuss methods and tools helping to achieve these goals. The core is the development and application of machine processable (semantic) annotations of online resources as well as their aggregation in large Knowledge Graphs. Only this enables bots to not only understand a question but being able to answer a question in a knowledgeable way and to organize a useful dialogue. We discuss the process of knowledge generation, hosting, curation, and deployment focusing on the usage of the knowledge graph to support dialog-based interfaces. We also provide an outlook on the broader application context of cyperphysical systems and physical agents."

Dieter Fensel is a full professor at University of Innsbruck. In 1989, he earned both his Master in Social Science (Free University of Berlin) and his Master in Computer Science (Technical University of Berlin). In 1993, he was awarded his Doctoral degree in Economic Science, Dr. rer. pol. (Doctorate on Political Science), from the University of Karlsruhe. In 1998 he received his Habilitation in Applied Computer Science. Throughout his doctoral and post-doctoral career, Prof. Dr. Fensel has held positions at the University of Karlsruhe (AIFB), the University of Amsterdam (UvA), and the Vrije Universiteit Amsterdam (VU). In 2002, he took up a Chair of Computer Science at the University of Innsbruck, Austria. In 2003, after receiving a large grant acquired from the Science Foundation Ireland (SFI), he founded the Digital Enterprise Research Institute (DERI) at the National University of Ireland, Galway, and became the Scientific Director. In 2006 he created the Digital Enterprise Research Institute (DERI) at the University of Innsbruck, Austria, and became the Founding Director. In 2007, he established the Semantic Technology Institute International (STI2), which is organized as a collaborative association of interested scientific, industrial and governmental parties of the world wide Semantic Web and Service community that share a common vision. His current research interests focus upon the usage of semantics in 21st century computer science. He is co-founder of several scientific events such as the European Semantic Web Conference Series (ESWC), International Semantic Web Conference Series (ISWC), and the TourismFastForward (TFF). He has published over 300 papers via scientific books and journals, conferences, and workshop contributions. He has co-organized over 300 academic events, and is an associated editor of various special issues of scientific journals.

Abstract: Multi-label classification (MLC) generalizes standard multi-class classification by relaxing the assumption of mutual exclusiveness of classes. Thus, instead of being assigned to exactly one category, each instance can be associated with several class labels simultaneously. While a broad spectrum of machine learning methods has been developed to tackle MLC problems, rule-based learning algorithms have a number of appealing properties that are not, at least not as a whole, shared by other approaches. This includes the potential interpretability of rules, their ability to model (local) label dependencies in a flexible way, and the facile customization of a predictor to different loss functions. In this talk, we discuss a modular framework for rule-based MLC and highlight important challenges in multi-label rule learning.

Eyke Hüllermeier is a full professor in the Heinz Nixdorf Institute and the Department of Computer Science at Paderborn University, Germany, where he heads the Intelligent Systems and Machine Learning Group. He studied mathematics and business computing, received his PhD in computer science from the Paderborn University in 1997, and a Habilitation degree in 2002. Prior to returning to Paderborn in 2014, he spent two years as a Marie Curie fellow at the Institut de Recherche en Informatique de Toulouse (France) and held professorships at the Universities of Dortmund, Magdeburg and Marburg. His research interests are centered around methods and theoretical foundations of artificial intelligence, with a specific focus on machine learning and reasoning under uncertainty. He has published more than 300 articles on these topics in top-tier journals and major international conferences, and several of his contributions have been recognized with scientific awards. Professor Hüllermeier is Co-Editor-in-Chief of Fuzzy Sets and Systems and serves on the editorial board of various other journals, including Machine Learning, Data Mining and Knowledge Discovery, and the International Journal of Approximate Reasoning. He is a coordinator of the EUSFLAT working group on Machine Learning and Data Mining and the head of the IEEE CIS Task Force on Machine Learning.

Abstract: A requirement is a statement to the effect that some function or quality shall be fulfilled. The concept of a requirement, and the associated concept of verification, invites to an interpretation in logical terms, with requirement statements represented as rules and verification conditions expressed in terms of logical consequence. This talk presents an industry-driven initiative to implement this idea for the digitalization of requirement handling along the value chain of the oil and gas industry. I will sketch an OWL-based approach that is simple, standards-based and scalable, features that are vital for implementation in industry. I will then address shortcomings of the model along with constraints for solutions.

Arild Waaler is director of SIRIUS - Centre for Scalable Data Access in the Oil and Gas Domain. He has over the last decade been a driving force for establishing collaboration between academic research and industry, both initiating and leading several cross-sector projects including Optique. His main research interest lies in the Knowledge representation. After finishing a civ.ing. degree in 1989, Waaler graduated as dr. philos in Philosophy in 1995 within Philosophical logic. He then had research positions at University of Oxford, in logic, and at the Søren Kierkegaard Research Centre in Copenhagen with a project in the History of Philosophy. After a teaching post at Finnmark College, Waaler became professor at the Dept. of Informatics at University of Oslo in 2006, when he started to build up the collaboration that eventually turned into SIRIUS.