Multi-level modeling is a novel modeling paradigm that aims to overcome limitations of standard modeling languages like the UML. As oppposed to such standard, “two-level”, languages, multi-level modeling languages allow for an unbounded number of classification levels. This should support avoiding conceptual redundancy as well as integrity threats and contribute to improved reusability and integration (cf. Frank 2022). 

Multi-level modeling was explicitly introduced in 2001 by Atkinson and Kühne as a means to overcome limitations of the UML. Since then, many diverse approaches to multi-level modeling have been developed with different central design objectives. For example, DeepTelos intends to support multi-level database systems. The Level-Agnostic Modeling Language (LML), Domain-Modeling Language (DML) and Flexible Multi-Level Model and Execution Language (FMMLx) aim to support multi-level software engineering. Languages such as Multi-Level Theory (MLT*) or Dynamic Multi-Level Algebra (DMLA) focus on multi-level knowledge representation. The heterogeneous landscape of multi-level modeling languages and tools poses an obstacle to the further progression of the field since, e.g., researchers invest much time into advancing only one language or tool. 

This Bachelor thesis sets out to analyze and compare a previously agreed upon set of multi-level modeling languages and tools. The main objective is to identify commonalties and differences among the selected languages and analyze the underlying assumptions and rationales. 

Literature:

• Atkinson C, Kühne T (2001) The Essence of Multilevel Metamodeling. UML 2001 - The Unified Modeling Language. Modeling Languages, Concepts, and Tools: 4th International Conference, Toronto, Canada, October 1-5, 2001. Proceedings, pp 19–33
• Atkinson C, Gerbig R, Kühl N (2014) Comparing Multi-Level Modeling Approaches. MULTI 2014: Proceedings of the Workshop on Multi-Level Modelling co-located with ACM/IEEE 17th International Conference on Model Driven Engineering Languages & Systems (MoDELS 2014), pp 53–61
• Frank U (2014) Multilevel Modeling: Toward a New Paradigm of Conceptual Modeling and Information Systems Design. Business and Information Systems Engineering 6(6):319–337
• Frank U (2022) Multi-Level Modeling: Cornerstones of a Rationale. Software and Systems Modeling 21:451–480
• Jeusfeld MA, Frank U (2021) Unifying Multi-Level Modeling: A Position Paper. 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pp 536–540

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

Die Welt lässt sich als eine Menge interagierender Objekte betrachten. Diese Prämisse liegt der objektorientierten (OO) Programmierung zugrunde. Spätestens mit der Veröffentlichung von Smalltalk in den 1980er-Jahren hat sich die OO-Programmierung wachsender Beliebeit erfreut. Das hatte auch die Entwicklung von einer Vielzahl an OO-Modellierungssprachen und OO-Modellierungsmethoden zur Folge, die 1997 in die Standardisierung der UML mündete. Ein Kernkonzept der Objektorientierung ist dabei die sogennante “Klasse”. Eine Klasse dient als Schablone für Objekte; Objekte werden fachsprachlich von einer Klasse instanziiert. In gängigen Programmierungssprachen wie Java oder Python muss die Definition einer Klasse daher immer vor der Instanziierung von Objekten erfolgen. Dieser Schritt wird jedoch kritisiert: Für Menschen sei primär der Umgang mit Objekten natürlich - die Definition von Klassen stelle eine Herauforderung dar (vgl. Bergstein and Lieberherr 1991).

In den letzten Jahren hat in der konzeptuellen Modellierung die sog. “flexible Modellierung” oder auch “Bottom-Up-Modellierung” vermehrt an Resonanz erfahren. Auch hier wird die Notwendigkeit der strikten Top-Down-Modellierung kritisiert: Intuitiver sei es, Nutzern die Modellierung auf niedrigeren Ebenen zu ermöglichen. Der Bedarf nach Klassifikation von Objekten ist dabei nur einer von vielen Problembereichen.

Diese Bachelor-Thesis soll sich dem Problem der automatisierten Klassen-Induktion für die konzeptuelle Modellierung annähern. Technische Schwierigkeiten die bei der Programmierung aufkommen würden sind auszuklammern. Dabei kann entweder eine vergleichende Untersuchung existierender Ansätze vorgenommen werden oder es kann ein eigener Ansatz zur Klasseninduktion entwickelt werden. Für Letzteres könnten wir Ressourcen bereitstellen, auf denen aufgebaut werden kann. Details und genaue Themenausrichtung sind in Betreuungsgesprächen zu klären. In jedem Fall steht die Identifiaktion von Herausforderungen und eine Identifikation und Bewertung möglicher Gegenmaßnahmen im Kern der Thesis.

Literatur:

• Bergstein PL, Lieberherr KJ (1991) Incremental Class Dictionary Learning and Optimization. ECOOP '91 European Conference on Object-Oriented Programming: Geneva, Switzerland, July 15-19, 1991. Proceedings, pp 377–396
• Façanha RL, Cavalcanti MC (2014) On the Road to Bring Government Legacy Systems Data Schemas to Public Access. Proceedings of the 1st Joint Workshop ONTO.COM / ODISE on Ontologies in Conceptual Modeling and Information Systems Engineering co-located with 8th International Conference on Formal Ontology in Information Systems
• Guerra E, de Lara J (2018) On the Quest for Flexible Modelling. MODELS '18: 18th Intenational ACM/IEEE Conference on Model Driven Engineering Languages and Systems, pp 23–33
• Kessentini W, Alizadeh V (2022) Semi-Automated Metamodel/Model Co-Evolution: A Multi-Level Interactive Approach. Software and Systems Modeling 21:1853–1876
• Töpel D, Kaczmarek-Heß M (2022) Towards Flexible Creation of Multi-Level Models: Bottom-Up Change Support in the Modeling and Programming Environment XModeler. MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, pp 404–413

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