- Introduces models and modelling processes to improve analytical skills and precision;
- Describes and compares five modelling approaches: mind-maps, models in biology, conceptual data models, ontologies, and ontology;
- Aims at readers looking for a digestible introduction to information modelling and knowledge representation.
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?
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
2 Mind Maps
2.1 What Are Mind Maps?
2.2 How to Create a Mind Map
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
4 Conceptual Data Models
4.1 What Is a Conceptual Data Model?
4.2 How to Develop a Conceptual Data Model
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
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
7 Fit For Purpose
7.1 A Beauty Contest
7.2 Ethics and Modelling
7.3 Design Your Own Modelling Language
8 Go Forth and Model
Where to buy itKey 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:
- The publisher: Springer or Springer Professional
- Amazon: .com (or as ebook) and on their country-specific sites, such as .uk, .de etc.
- Many national online bookstores, such as bol, booktopia, and indigo.
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
“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 bookRecent interviews and magazine articles online that relate to the book:
- An illustration of an ontological investigation (in relation to chapter 6 and on a different topic than discussed in the book): A computer science technique could help gauge when the pandemic is 'over', which was published in The Conversation on 20 March 2022 and republised widely, including by The Next Web and The Raw Story
- A dialogue/interview by Teodora Petkova: Ontology Engineering and the Love for Modeling and Analysis: A Dialogue with Maria Keet. January 2022
- On Comparing modelling languages, guest post at Jordi Cabot's MOdeling LAnguages blog; 6 February 2024.
- An illustration of an “ERDP” to create an EER diagram: the dance school database; 8 October 2023
- Systematic design of conceptual modelling languages; 20 April 2023
- ChatGPT, deep learning and the like do not make ontologies (and the rest of AI) obsolete; 31 January 2023
- How does one do an ontological investigation? 9 September 2022
- I've written introductory/overview blog posts over the years for many papers that I (co-)authored and referenced in the book, which can be accessed here
Supplementary materialThere 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.
- An illustration of the model design procedure for a dance school database, as promised in the "Dance and Conceptual Data Models" section in Chapter 4
- The OWL file of the 'version 0.1' draft ontology developed and compared to the mind map and the conceptual data model in subsection 22.214.171.124 "A Task-Based Comparison: Learning About Migrant Labour". The content is based on the first page of Section 5 of Xulu-Gama et al's book chapter "Policy implementation challenges for worker education and foreign national migrants".
- More material will be uploaded soon, including, among others, a sketch of the dance ontology discussed in Section 5.3.