Skip to main content
Top

2023 | Book

The What and How of Modelling Information and Knowledge

From Mind Maps to Ontologies

insite
SEARCH

About this book

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 to ontology 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 book will serve them well.

Table of Contents

Frontmatter
1. Introduction: Why Modelling?
Abstract
Models and modelling evoke a myriad of images and ideas, as they can refer to many different things and processes. This chapter first differentiates between four categories of models: physical models, mathematical models, large data-driven models, and conceptual models. It is the latter category that is the focus of this book. The second part of the chapter outlines why they are important and provides an outline of what will be covered in the remainder of the book.
C. Maria Keet
2. Mind Maps
Abstract
A basic approach for structuring content is to draw a mind map. With its central concept and colourful branches of related things, mind maps are perceived as a useful mechanism in education and business. But what does a good mind map look like and how to create one? Does the evidence show they are beneficial? In this chapter we will take a closer look at mind maps, on what they are and their typical size and shape. It also introduces the running example domain of dance, which will be revisited in successive chapters. The research shows mind maps can be helpful in certain settings and for certain topics, such as primarily early years in school, but not in others. The more you know of a subject domain and the more you want to do, the more there is that needs to be modelled, however, which is when you’re going to have to shop elsewhere to meet the modelling requirements.
C. Maria Keet
3. Models and Diagrams in Biology
Abstract
Diagrams and models in biology, loosely called ‘biological models’, enjoy a freedom of notation unlike mind maps. They are ubiquitous in school books and all the way up into scientific publications. While they might look like cartoons to some, there is a lot more to them than meets the eye on cursory glance. By means of introduction in two illustrations with fermentation of sugars and plankton in the ocean, we’ll proceed to take a look at the systematics behind such models and scientific theories that can be embedded in them, as the cladists do in their cladograms. The second part of the chapter proposes a procedure for how to create such biology diagrams. This is then illustrated for dance, and dancing lyrebirds in particular. While biological models solve the limitations of mind maps, moving the goalposts brings afore new limitations and challenges, with which we close the chapter.
C. Maria Keet
4. Conceptual Data Models
Abstract
The approach of diagramming or modelling conventions for each subject domain that we saw for biological models doesn’t scale to all subject domains. Yet, other disciplines use or want to use models too. To overcome this challenge, we take a turn into computing and application development, since clearly what they do is being used across very many subject domains and it works somehow. The solution advanced here is conceptual data modelling languages for database and application design. This chapter will first describe one such modelling strategy, being Entity-Relationship diagrams with its extension and two similar modelling languages that capture the declarative ‘what’ of a domain. This is richly illustrated with examples on topics such as books, management, cars, and molecules. Computing has tried and tested procedures for developing such models, including the Conceptual Schema Design Procedure, which will be described afterwards. The main procedures are illustrated with conceptual model development for a prospective scientific database about data for our dancing lyrebirds. While solving limitations of biology diagrams, a changing Information Technology landscape may be demanding more than traditional conceptual data models currently deliver, and so also here, we close with a brief section on limitations.
C. Maria Keet
5. Ontologies and Similar Artefacts
Abstract
While conceptual data models offered a new machinery for modelling, it implicitly assumes database and application design and they’re mostly just diagrams. What if we could create a model for a subject domain, where the model’s content holds across applications and we can make it do various things on the computer to make systems behave ‘intelligently’ or at least better than without such a model? The answer to that is: yes, we can do this, with ontologies. We will first look at what they are, including a gentle introduction to their formal underpinnings with logic and automated reasoning. This is followed by three examples of success stories and varied usages: data integration with the Gene Ontology, uncovering novel knowledge about enzymes outperforming the scientists, and automating educational question generation and marking of the exercises. Second, since their development is nontrivial, there are many methods and procedures to develop ontologies, including top-down and bottom-up approaches. We return to dance to illustrate a sample of those methods, by taking steps to improve a salsa dance ontology. It is possible to complain about limitations also for ontologies, with which we will close the chapter.
C. Maria Keet
6. Ontology—With a Capital O
Abstract
The notion of ontologies in computing and Information Technology was borrowed and adapted from Ontology in philosophy. What do the philosophers do when they are modelling? And could a modelling approach unrestrained by computational practicalities offer the ultimate modelling solution? In this chapter, we first trace the origins of Ontology. To gain an impression of ontology, we will revisit and refine the aggregation association of the Unified Modelling Language and the Gene Ontology’s part-of, which turns up in Ontology as mereology, and consider stuff, like lemonade and petroleum, and present model for that, too. The two examples also illustrate different approaches to how to conduct an ontological investigation, which will also be introduced in the chapter. This is then applied to the investigation into the ontology of dance. Ontology can solve some issues in modelling that were difficult to do with the types of models we have discussed so far, but, depending on your aims, limitations can be formulated.
C. Maria Keet
7. Fit For Purpose
Abstract
Different types of declarative models passed the revue, yet all of them had limitations. Is there one ‘best’ among mind maps, biological models, conceptual data models, ontologies, and theories in Ontology? What if you didn’t like either one fully, could you design your own one, and if so, how? Do any of them have ethical issues in their design or use, alike the ‘AI ethics’ fails? No. Yes. And sort of. We begin this chapter with a feature comparison of the types of models we covered and add two example-based comparisons to that, on all those models about dance and a task-based evaluation on learning from a textbook passage about labour migration. Second, the notion of ethics is addressed in the sense of both professions practice and potential pitfalls in modelling that would be explicit biases once you know what to look out for. Third, we’ll step through a procedure how you could develop a new modelling language for the purpose you like most.
C. Maria Keet
8. Go Forth and Model
Abstract
This short chapter wraps up with conclusions and some reflections, and by reconsidering the questions posed in the Introduction to answer them.
C. Maria Keet
Backmatter
Metadata
Title
The What and How of Modelling Information and Knowledge
Author
C. Maria Keet
Copyright Year
2023
Electronic ISBN
978-3-031-39695-3
Print ISBN
978-3-031-39694-6
DOI
https://doi.org/10.1007/978-3-031-39695-3

Premium Partner