The What and How of Modelling Information and Knowledge

The main aim of this book is to introduce a group of models and modelling of information and knowledge comprehensibly. Such models and the processes for how to create them help to improve the skills to analyse and structure thoughts and ideas, to become more precise, to gain a deeper understanding of the matter being modelled, and to assist with specific tasks where modelling helps, such as reading comprehension and summarisation of text. The book draws ideas and transferrable approaches from the plethora of types of models and the methods, techniques, tools, procedures, and methodologies to create them in computer science.

This book covers five principal declarative modelling approaches to model information and knowledge for different, yet related, purposes. It starts with entry-level mind mapping, to proceed to biological models and diagrams, onward to conceptual data models in software development, and from there to ontologies in artificial intelligence and all the way toontology in philosophy. Each successive chapter about a type of model solves limitations of the preceding one and turns up the analytical skills a notch. These what-and-how for each type of model is followed by an integrative chapter that ties them together, comparing their strengths and key characteristics, ethics in modelling, and how to design a modelling language. In so doing, we’ll address key questions such as: what type of models are there? How do you build one? What can you do with a model? Which type of model is best for what purpose? Why do all that modelling?

The intended audience for this book is professionals, students, and academics in disciplines where systematic information modelling and knowledge representation is much less common than in computing, such as in commerce, biology, law, and humanities. And if a computer science student or a software developer needs a quick refresher on conceptual data models or a short solid overview of ontologies, then this bookwill serve them well.

“The book describes – in excellent style and appropriate framing and leveling - five principal declarative modelling approaches to model information and knowledge for different, yet related, purposes. … The book is rich on good advice going down a couple of levels, also on the complicated matters. You will learn about how-to as well as why.” (Thomas Frisendal, linkedin.com, January 10, 2024)

Maria Keet is an Associate Professor with the Department of Computer Science at the University of Cape Town, South Africa. Her research focuses on ontology engineering, conceptual data models, and natural language generation within the area of knowledge engineering, which has resulted in some 150 publications, including an award-winning textbook on ontology engineering and several best paper awards. She has been Principal Investigator and participant in several research projects funded by the South African National Research Foundation, the European Union, and Department of Science and Technology.