Masterclass

The masterclasses will take place on 3 March 2025.

Giancarlo Guizzardi: "Data Semantics, Explanation and Interoperability"

It is well-known by now that, of the so-called 4Vs of Big Data (Velocity, Volume, Variety and Veracity), the bulk of effort and challenge is in the latter two: (1) data comes in a large variety of representations (both from a syntactic and semantic point of view); (2) data can only be useful if truthful to the part of reality that it is supposed to represent. Moreover, the most relevant questions we need to have answered in science, government and organizations can only be answered if we put together data that reside in different data silos, which are produced in a concurrent manner by different agents and in different points of time and space. Thus, data is only useful in practice if it can (semantically) interoperate with other data. Every data schema represents a certain conceptualization, i.e., it makes an ontological commitment to a certain worldview. Issue (2) is about understanding the relation between data schemas and their underlying conceptualizations. Issue (1) is about safely connecting these different conceptualisations represented in different schemas. To address (1) and (2), we need to be able to properly explain these data schemas, i.e., to reveal the real-world semantics (or the ontological commitments) behind them.  In this masterclass, I discuss the strong relation between the notions of real-world semantics, ontology, and explanation. I will present a notion of explanation termed Ontological Unpacking, which aims at explaining symbolic representation artifacts (conceptual models connected to data schemas, knowledge graphs, logical specifications). I show that these artifacts when produced by Ontological Unpacking differ from their traditional counterparts not only in their expressivity but also on their nature: while the latter typically merely have a descriptive nature, the former have an explanatory one. Moreover, I show that it is exactly this explanatory nature that is required for semantic interoperability. I will also discuss the relation between Ontological Unpacking and other forms of explanation in philosophy and science, as well as in Artificial Intelligence. I will argue that the current trend in XAI (Explainable AI) in which “to explain is to produce a symbolic artifact” (e.g., a decision tree or a counterfactual description) is an incomplete project resting on a false assumption, that these artifacts are not “inherently interpretable”, and that they should be taken as the beginning of the road to explanation, not the end. Finally, I will illustrate our approach with the unpacking of a prominent Viral Conceptual Model (VCM). This masterclass is based on the paper: 

Guizzardi, G., & Guarino, N. (2024). Explanation, semantics, and ontologyData & Knowledge Engineering153, 102325.

  • About Giancarlo

    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 has also been a Guest Professor at Stockholm University (Sweden), the Technical University of Vienna (Austria), the Prague School of Economics (Czech Republic), and the University of Trento (Italy). He has been active for nearly three decades in the areas of Formal and Applied Ontology, Conceptual Modeling, Enterprise Computing and Information Systems Engineering, working with a multidisciplinary approach in Computer Science that aggregates results from Philosophy, Cognitive Science, Logics and Linguistics. He is the main contributor to the Unified Foundational Ontology (UFO) and to the OntoUML modeling language. Over the years, he has invited to deliver keynote speeches in several key international conferences in these fields (e.g., ER, CAiSE, BPM, RCIS, IEEE ICSC). 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. He is currently the Chair of the Steering Committee of the International Conference on Conceptual Modeling (ER), a member of the Steering Committees of CAiSE, EDOC, and IEEE CBI, and a member of the Advisory Board of the International Association for Ontology and its Applications (IAOA). Finally, he is an ER fellow.

Roel Wieringa: "Digital Business Ecosystems"

In this master class I show what the role of ecosystems in business innovation and digital transformation is. We provide a definition and give examples of digital business ecosystems and show how to develop and test business models for ecosystems. Then, in three steps, we develop a value proposition for an ecosystem, design the value network to deliver it, assess its revenue-generating potential, and map it to the enterprise architecture of participating companies.

Digital business ecosystems

Business ecosystems are networks of economic entities that depend on each other for their survival and well-being. Companies in a business ecosystem collaborate and compete with each other to deliver value to customers. 

Digital business ecosystems are IT-enabled business ecosystems. Examples are online marketplaces, smart industry networks, ride hailing systems, and social networks. They consist of buyers, sellers, advertisers, riders, drivers, publishers, smart product manufacturers, cloud service providers and many others whose collective collaboration and competition delivers services online to customers.

AI applications too are part of digital business ecosystems.  They depend on a network of sensors, smart products, wireless networks, mobile devices, and other entities that are owned or operated by independent economic entities. The network is needed for data collection. Without it, there would be no data to train machine learning systems on. The users of the AI system are also part of the network.

Business models

These business networks cannot survive if there is no viable business model for them. A business model of a digital business ecosystem must show how each business in the network creates value for the network, how the network delivers value to customers, and how each business captures value (revenue) from this. This is not a process model, because it is not a description of operational activities and their coordination. The business model of a network shows what value objects (products, services or money) are exchanged by the participants of a network to satisfy customer needs. It is the basis for the assessment of commercial viability as well as for the identification of requirements for coordination processes, data sharing, and enterprise architectures.

Identifying the business model of a network is crucial to understanding whether the network will survive and thrive. Too often, enterprises try to adopt new technology like blockchain, machine learning or smart product technology because it is hip and hot, without a tested business model that describes how participants sustain themselves when they contribute value to the network. And without a viable business model, technology will fall flat on the ground. Digital business ecosystems need a viable business model.

In addition, once a viable business model of a digital business ecosystem has been agreed on, the business model can be used as a source of requirements on enterprise architecture, coordination technology, and data sharing.

This master class is based on our recent book [1].

References

  1. R. Wieringa en J. Gordijn, Digital Business Ecosystems. How to Create, Deliver and Capture Value in Business Networks, TVE Press, 2023. 
  2. M. Zeng, Smart Business. What Alibaba's Success Reveals About the Future of Strategy, Harvard Business Review Press, 2018.
  3. J. Gordijn en R. Wieringa, E3value User Guide. Designing Your Ecosystem in a Digital World, TVE Press, 2021.
  • About Roel

    Roel Wieringa is emeritus Chair of Information Systems at the University of Twente, The Netherlands and managing partner of The Value Engineers. His research topics as Chair ranged from formal specification, conceptual modeling, requirements engineering to risk analysis of information systems. He is the author of Requirements Engineering: Frameworks for Understanding (Wiley, 1996) and Design Methods for Reactive Systems: Yourdon, Statemate and the UML (Morgan Kaufmann, 2003). In the past 20 years his research also included research methodology for information systems engineering and he published a book about this, Design Science Methodology for Information Systems and Software Engineering (Springer, 2014). 

    In The Value Engineers, Roel helps companies design their digital ecosystem strategy (www.thevalueengineers.nl), and he is very busy writing blogs about business models, business ecosystems, and the networked society.

  • Masterclass contents

    This masterclass will be structured as follows:

    Topic

    Duration (minutes)

    Description

    Introduction 

    10

     

    Digital ecosystems, platforms, and smart networks

    20

    Definitions and examples from the digital economy

    Business models

    30

    The business model wheel: value proposition, contribution network, revenue model, delivery model with examples.

    Technology-driven value proposition design

    40

    Value proposition structure and design heuristics. Examples and a small exercise.

    Revenue model design for smart networks

    40

    Examples of networked revenue models.

    Mapping to enterprise achitecture

    30

    Guidelines for deriving EA. Examples.

    Wrap-up

    10

     

    Total 

    180