Information systems can be considered linguistic artifacts (Stamper 1987, Ortner 1993, Frank 2021). They are constituted through software languages and can only be used if they represent concepts prospective users are familiar with. As a result, the integration of information systems can be considered a semantic issue, too (Frank 2008): Different information systems may utilize various domain concepts in different formats, but still must be enabled to effectively and efficiently communicate with each other.

Integration continues to be an issue for many corporations across various domains and industries, caused, among other reasons, by an increasing number of heterogeneous vendors each of which uses its own domain language. Resulting systems communication issues are addressed by various means, e.g, by boling down all concepts to a “global schema” which the concepts used in another information system must be mapped to. Existing solutions are, however, faced with various insufficiencies and may lead to conceptual redundancy, error-prone semantic reconstruction efforts, and miscommunication between systems. These insufficiencies threathen the integrity of information systems and, with that, their effective and efficient use in organizations.

Existing technical landscapes are often based on so-called two-level software languages, such as Java, C#, Python, UML, or the ERM language (cf. Kühne 2007, Atkinson and Kühne 2008). Two-level languages provide developers with control over two levels of abstraction: a type level and an instance level. In object-oriented development, this corresponds to classes and objects. The dominant two-level development style prohibits the use of further abstraction levels to facilitate the communication between information systems: all communication is restricted to a type and an instance level.

This restriction is alleviated in multi-level software languages, which, among others, allow for the definition of an unbounded number of classification levels. Multi-level software languages have been motivated by limitations of two-level languages in various application scenarios, among the issues of integration with two-level languages outlined above (Frank 2022). However, apart from theoretical discussions about potential prospects of using multi-level languages for the integration of information systems, no detailed conception of how to apply multi-level languages for integration has yet been elaborated. As part of this thesis, you are asked to investigate in detail when and how multi-level languages may aid integration issues, what obstacles arise, and how they might be counteracted. 

The thesis is part of an ongoing research project with Oracle. As part of thesis, students may be granted an internship at Oracle, providing access to Oracle’s huge data sources which may be used to conduct experiments. Proficiency in English is a prequisite for this. 

Application Deadline: 30 September 2025. Application process will be closed as soon as a suited candidate is found. You can submit your application by sending a short statement of motivation, your current transcript of records, and your CV to pierre.maier (at) uni-due.de AND Sekretariat.IIS (at) icb.uni-due.de. 

• Atkinson C, Kühne T (2008) Reducing Accidental Complexity in Domain Models. Software and Systems Modeling 7:345–359
• Frank U (2008) Integration: Reflections on a Pivotal Concept for Designing and Evaluating Information Systems. Information Systems and e-Business Technologies: 2nd International United Information Systems Conference, UNISCON 2008, Klagenfurt, Austria, April 22-25, 2008, Proceedings, pp 111–122
• Frank U (2021) Language, Change, and Possible Worlds: Philosophical Considerations of the Digital Transformation. In: Siegetsleitner A, Oberprantacher A, Frick M-L, Metschl U (eds). Crisis and Critique: Philosophical Analysis of Current Events, Proceedings of the 42nd International Wittgenstein Symposium. De Gruyter: Berlin, Boston, MA, pp 117–138
• Frank U (2022) Multi-Level Modeling: Cornerstones of a Rationale. Software and Systems Modeling 21:451–480
• Frank U, Töpel D (2020) Contingent Level Classes: Motivation, Conceptualization, Modeling Guidelines, and Implications for Model Management. MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
• Kühne T, Schreiber D (2007) Can Programming be Liberated from the Two-Level Style? Multi-Level Programming with DeepJava. OOPSLA '07: Companion to the 22nd ACM SIGPLAN Conference on Object-oriented Programming Systems and Applications Companion, pp 229–244
• Ortner E (1993) Software-Engineering als Sprachkritik: Die Sprachkritische Methode des Fachlichen Software-Entwurfs. Universitätsverlag Konstanz: Konstanz
• Stamper R (1987) Semantics. In: Boland RJ, Hirschheim R (eds). Critical Issues in Information Systems Research. John Wiley & Sons: Chichester, pp 43–78

Artificial neural networks (ANNs) denote a popular class of models used within machine learning. An ANN typically consists of multiple layers of simple processing units, so-called artificial neurons. Most current ANNs involve multiple layers of these processing units, hence the term deep learning is sometimes applied to describe them. Historically, they emerged from a neurophysiological inspiration to express the processing of mammal neurons in mathematical terms (cf. McCulloch and Pitts 1943). There exists a plethora of different approaches to the design of ANNs, some variations include the number of artificial neurons in a layer, the activation function applied, or the connection of artificial neurons between layers. From these variations have emerged several classes of ANN architectures, such as Multi-Layered Perceptrons (MLPs), Generative Adversial Networks (GANs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or more recently Transformers. It is conspicuous that many papers, which discuss a particular ANN architecture, represent them in some diagrammatic form. This diagrammatic representation, however, does not follow any unified structure. This results in two challenges: First, ANNs are not visually comparable through an analysis of their diagrammatic representations. Second, the depicted diagrams of ANNs might lack relevant information, overseen by the original researchers. In short: It appears that the depiction of ANNs lack a conceptual modeling language.

The present thesis should adress this gap. Therefore, it is relevant to expound on the foundations and variations of ANNs as well as to explore the fundamentals of conceptual modeling languages. Based on an analysis of the design, evaluation, and application of ANNs, requirements for a corresponding modeling method should be derived. Thereupon, these insights should be used to specify a conceptual modeling method for ANNs.

Literature:

• Aggarwal CC (2018) Neural Networks and Deep Learning: A Textbook. Springer International Publishing: Cham
• Du K-L, Swamy MNS (2014) Neural Networks and Statistical Learning. Springer-Verlag: London
• Frank U (2013) Domain-Specific Modeling Languages – Requirements Analysis and Design Guidelines. In: Reinhartz-Berger I, Sturm A, Clark T, Wand Y, Cohen S, Bettin J (eds.) Domain Engineering: Product Lines, Conceptual Models, and Languages. Springer: Cham, pp. 133-157
• Kelleher JD (2019) Deep Learning. The MIT Press: Cambridge, MA, London
• McCulloch WS, Pitts W (1943) A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5:115-133

If language shapes our reality, changing our language might lead to a different, potentially preferable reality. This thought is echoed throughout a variety of philosophical schools and can, in different variations, with different assumptions, and with different implications, be found in the writings of Ludwig Wittgenstein, Richard Rorty, Friedrich Nietzsche, Immanuel Kant, or Humberto Maturana. Recently, the discussion has received more widespread attention. Motivated in part from feminist philosophy of the 1990s, philosophers have combined their research efforts towards the improvement of language under the moniker of conceptual engineering and conceptual ethics. The amelioration of concepts and language is faced with several theoretical and practical challenges. What makes a concept “better” than another? How could a new concept be adopted by respective language users?

Information systems development is essentially concerned with language development (clarification and sources per request). Broadly, this poses the question if information systems can support conceptual engineering and, if so, in what regards. Machine learning (ML) might be a fruitful first step to guide this analysis. Contemporary ML approaches are inductive (cf. Rescher 1980): they generate potentially novel generalizations based on a set of observations. Researchers like Rees (2022) therefore suggest that they might guide the development of novel concepts.

This master’s thesis should explore the capabilities of ML to support conceptual engineering. You should identify potential tasks of conceptual engineering and what requirements they face. Then you should investigate how different ML approaches (we can disucss which in our first meetings) can serve to address these requirements.

Literature:

• Burgess A, Cappelen H, Plunkett D (eds) (2020) Conceptual Engineering and Conceptual Ethics. Oxford University Press: Oxford
• Butlin P (2021) Sharing Our Concepts with Machines. Erkenntnis
• Cappelen H, Dever J (2019) Bad Language. Oxford University Press: Oxford
• Haslanger S (2012) Resisting Reality: Social Construction and Social Critique. Oxford University Press: Oxford
• Medin DL, Smith EE (1984) Concepts and Concept Formation. Annual Review of Psychology 35(35):113–138
• Montemayor C (2021) Language and Intelligence. Minds and Machines 31:471–486
• Ontañón S, Dellunde P, Godo L, Plaza E (2012) A Defeasible Reasoning Model of Inductive Concept Learning from Examples and Communication. Artificial Intelligence 193:129–148
• Rees T (2022) Non-Human Words: On GPT-3 as a Philosophical Library. Daedalus 151(2):168–182
• Rescher N (1980) Induction: An Essay on the Justification of Inductive Reasoning. Basil Blackwell: Oxford