This book:


Aims and content

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. It 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?


Intended audience

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

1 Introduction: Why Modelling?
  1.1 What Is a Model?
  1.2 Not All Models Are Equal
  1.3 The Plan
  References
2 Mind Maps
  2.1 What Are Mind Maps?
  2.2 How to Create a Mind Map
  2.3 Limitations
  References
3 Models and Diagrams in Biology
  3.1 Reading a Diagram: Two Examples
  3.2 A Quest for Common Characteristics
  3.3 How to Create a Biological Diagram
  3.4 Limitations
  References
4 Conceptual Data Models
  4.1 What Is a Conceptual Data Model?
  4.2 How to Develop a Conceptual Data Model
  4.3 Limitations
  References
5 Ontologies and Similar Artefacts
  5.1 What Is an Ontology, the Artefact?
  5.2 Success Stories of Using Ontologies
  5.3 Methodologies for Developing Ontologies
  5.4 Limitations
  References
6 Ontology—With a Capital O
  6.1 The Greeks and Then Some
  6.2 Examples: Parthood and Stuff
  6.3 How to Do an Ontological Investigation
  6.4 Limitations
  References
7 Fit For Purpose
  7.1 A Beauty Contest
  7.2 Ethics and Modelling
  7.3 Design Your Own Modelling Language
  References
8 Go Forth and Model
  Reference
Index


Where to buy it

Key data:
Keet, C.M. The What and How of Modelling Information and Knowledge: From Mind Maps to Ontologies. Springer. 2023. ISBN-10: 3031396944; ISBN-13: 978-3031396946

Currently, it is available as hardcopy and ebook from various online stores, among others:

Media

Reviews and endorsements

"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
            Graph Data Architect, Visual Data Modeler and GQL committee member
            Full review posted on LinkedIn as Interesting New Book on Modeling of Data and Information

"This book is highly recommended for people with any level of expertise, from novices (as the book is not too technical to scare you away) to advanced modellers (the book is not oversimplified either). If you are new to the topic of modelling, then the book will help you create your modeller mindset."
            ------- César Bernabé
            PhD candidate in the Biosemantics group at Leiden University Medical Centre
            Full review posted on his blog as A must-read for those interested in (conceptual) modelling!

"The most appealing for me is Maria’s riveting writing and also her deep knowledge being trasnfered through examples and bit by bit, evey step building on the previous one. Maria does have a nack for explaining complex stuff very methodically grounding it in theoretical knowledge without overwhelming the readers. Things are presented in layers do that you can choose how deep you want to go down the rabbit whole. And there are rabbit holes :) But modelled. :) with elaborate care."
            ------- Teodora Petkova
            Content writer and semantic medatada specialist with Ontotext
            More preliminary review comments posted on LinkedIn

"Trata-se de um bom livro, principalmente se você gosta de receber informação contextualizada, o que é uma preocupação da autora ao londo de toda obra."
            ------- Mauricio B. Almeida
            Full professor at the Federal University of Minas Gerais
            An Amazon review

“The pragmatic and historical approach of this book is of great help in developing a domain modelling framework for the Dutch Public Healthcare.”
            ------- Holke Visser
            Data architect at Visser Data BV, board member of Enterprise Engineering Institute
            Posted on LinkedIn

“I like the pragmatic guidance on modelling steps and approaches and the supporting examples, plus what you can and can’t do with different modelling types and how you can enhance readability and understandability of models and make implicit knowledge and inconsistent constructs in models visible, such that you can improve the quality of your models.”
            ------- Pieter van Everdingen
            Enterprise & Information Architect at OpenInc
            Posted on LinkedIn

“I’ve enjoyed reading your book," "There are many non-IT specialties across an organization that can benefit from a broad understanding of mapping, without having to understand the intricacies of formal modelling languages.”
            ------- Barbara Fillip, Ph.D., PMP
            Senior Advisor, Knowledge Management at Chemonics International
            Posted on LinkedIn

Articles related to the book

Recent interviews and magazine articles online that relate to the book: A selection of recent blog posts that were spin-offs of draft (sub)sections of the book, or are otherwise related:

Supplementary material

There are several 'extras' that may be of use that either would distract from the flow of the main text or pointless to include in a book, like sources files that runs over pages. They are indexed here and point to sub-pages of the book.