Academic Speakers
Prof. Dr. Knut Hinkelmann, FHNW – University of Applied Sciences Northwestern Switzerland (CH)
Title: Knowledge Matters: The Roles of Domain Knowledge for Creating and Interpreting Enterprise Models
Abstract: In this talk, I will deal with different roles of knowledge for enterprise modelling and suggest some areas for future research. Knowledge enables action. One kind of action in enterprise modelling is to create the models, which contain knowledge. During modelling, the modeller uses knowledge to create the model. Besides the knowledge that is finally represented in the model the modeller uses additional knowledge that allows to identify, which knowledge needs to be represented, and to determine, how to represent the knowledge. There are qualitative and quantitative methods to acquire this knowledge. The latter is related to machine learning and itself requires knowledge to apply the appropriate methods and to make an appropriate representation of the learnings. A model supports an actor to perform actions to achieve a goal. A model can be regarded as information describing a domain of interest. To use the model again requires knowledge. To avoid misunderstanding and misinterpretation, the actor using the model should the same knowledge for model interpretation as the modeller had used for model creation. In other words, model creator and model user should have a shared ontology. When using domain-specific modelling language, their metamodel represents part of this ontology. However, having a shared understanding of the application environment is still a challenge. This is even more challenging if the actors can be either human or machine.
Bio: Knut Hinkelmann is Professor for Information Systems and Head of the Master of Science in Business Information Systems at the FHNW University of Applied Sciences and Arts Northwestern Switzerland. Since 2015 he is a Research Associate at the University of Pretoria (South Africa) and since 2017 he is Adjunct Professor at the University of Camerino (Italy). In 1988 he obtained a diploma in Computer Science from the University of Kaiserslautern and a Ph.D. in Natural Sciences from the Computer Science Department of the same university in 1995. From 1988 to 1990 he was researcher at the Research Institute for Applied Knowledge Processing FAW in Ulm. From 1990 until 1998 he was researcher and later Head of the Knowledge Management research group at the German Research Center for Artificial Intelligence DFKI. From 1998 until 2000 he worked as product manager for Insiders Information Management GmbH. He joined the FHNW University of Applied Sciences and Arts Northwestern Switzerland in August 2000 as a professor for Information Systems and became head of study programs in 2002. He was CEO of the KIBG GmbH from 1996 until 1998; and from 2006 until 2012 he was Scientifc Advisor of STEAG & Partner AG. Furthermore he has been supervisor and external examiner of several PhD Theses at University of Camerino, University of South Africa, University of Pretoria and Cape Pensinsula University of Technology in Cape Town and guest lecturer at the University of Camerino and the University of Vienna.
Prof. Dr. Giancarlo Guizzardi, University of Twente (NL)
Title: Semantic Models for Trustworthy Systems: A Hybrid Intelligence Augmentation Program
Abstract: This year’s edition of CAISE has as its central theme the topic of cyber-human systems. These systems are formed by the coordinated interaction of human and computational components. In this talk, I will argue that these systems can only be designed as trustworthy systems if the interoperation between all these components (i.e., human-human, machine-machine, and human-machine) is meaning preserving. For that, we need to take the challenge of semantic interoperability between these components very seriously. I will discuss the notion of trustworthy semantic models and defend its essential role in addressing this challenge. Finally, I will advocate that engineering and evolving these semantic models as well as the languages in which they are produced requires a hybrid intelligence augmentation program (i.e., including human and machine intelligence, and the latter of different kinds) resting on a combination of techniques including formal ontology, logical representation and reasoning, crowd-sourced validation, and automated approaches to mining and learning.
Bio: Giancarlo Guizzardi is a Full Professor of Software Science and Evolution as well as Chair and Department Head of Semantics, Cybersecurity & Services (SCS) at the University of Twente, The Netherlands. He is also an Affiliated/Guest Professor at the Department of Computer and Systems Sciences (DSV) at Stockholm University, in Sweden. He has been active for nearly three decades in the areas of Formal and Applied Ontology, Conceptual Modelling, Business Informatics, and Information Systems Engineering, working with a multi-disciplinary approach in Computer Science that aggregates results from Philosophy, Cognitive Science, Logics and Linguistics. Over the years, he has delivered keynote speeches in several key international conferences in these fields (e.g., ER, BPM). In particular, he will deliver one of the CAISE 2023 keynote speeches on the relation between Semantics, Ontology, and Explanation. He is currently an associate editor of a number of journals including Applied Ontology and Data & Knowledge Engineering, a co-editor of the Lecture Notes in Business Information Processing series, and a member of several international journal editorial boards. Finally, he is a member of the Steering Committees of ER, EDOC, and IEEE CBI, and of the Advisory Board of the International Association for Ontology and its Applications (IAOA).
Industry Speaker
Dr. Evgeny Kharmalov, Bosch
Title: Neuro-Symbolic AI for Industry 4.0
Abstract: Neuro-Symbolic AI is a prominent field with significant attention in both academia and industry. It has roots in semantic technology and machine learning (ML) and aims at fusing them to benefit from both worlds. Indeed, semantics allows to capture domain knowledge and integrate data, ML allows to perform data analysis and predict; while their fusion allows to guide learning via symbolic knowledge and to extract structures from data via deep learning. Such fusion has a great potential for Industry 4.0 that aims at smart fully automated factories. Moreover, it has already been used in such context while the success is still limited due to a number of factors. In this keynote I will discuss the state of the art Neuro-Symbolic AI and touch bases on semantic-based industrial modeling, industrial analytics such as quality prediction in discrete manufacturing, exemplify this with Bosch manufacturing scenarios, and also discuss limitations of existing technology in, e.g., in scalability and uncertainty handling.
Bio: Evgeny is a Senior AI Expert at the Bosch Center for AI and an Associate Professor at the University of Oslo. He does AI-centered research that aims at sustainable, circular, and smart manufacturing / Industry 4.0 and centered around topics of standardized, intelligent and data-driven production value-chain empowered with digital twins and IoT. His research in particular accounts for Neuro Symbolic AI that embraces Semantic Technologies, ontologies, knowledge graphs for symbolic representation and reasoning over manufacturing knowledge, for machine learning for processing of production data, and for their combinations. Evgeny’s work led to 160+ publications and they were cited more than 4.3K times (according to Google Scholar). Evgeny is one of the 110 top AI German researchers according to study published at the Springer magazine in late 2022. He won several prestigious awards including the best research and industrial applications papers at ESWC’20, ISWC’17, best demo at ISWC’15, and he is ranked on top 10 of “AI 2000 Knowledge Engineering Most Influential Scholars” by AMiner. Evgeny raised or participated in raising of more than 4.5M EUR of research funding from EU, EPSRC and Royal Society.