The What and How of Modelling Information and Knowledge: From Mind Maps to Ontologies

✍ Scribed by C. Maria Keet

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 184
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Preface
Acknowledgements
Contents
About the Author
Acronyms
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?
2.1.1 On Determining Whether Mind Maps Are Beneficial
2.1.2 What the Researchers Observed
2.2 How to Create a Mind Map
2.2.1 Targeting for the Right Size and Shape
2.3 Limitations
References
3 Models and Diagrams in Biology
3.1 Reading a Diagram: Two Examples
3.1.1 Fermenting Sugars into Alcohol, Acids, and Gas
3.1.2 Who Eats Whom in the Ocean, at the Microscopic Level
3.2 A Quest for Common Characteristics
3.2.1 The Chemists and the Cladists
3.2.2 One Diagramming Language for all Biological Models
3.3 How to Create a Biological Diagram
3.4 Limitations
References
4 Conceptual Data Models
4.1 What Is a Conceptual Data Model?
4.1.1 The Beginnings—and Still Standing Strong: ER
4.1.2 Conceptual Data Modelling Explosion
4.1.2.1 A Temporal Extension to ER
4.1.2.2 Yet More Extensions
4.1.3 On Turf Wars and Truces
4.1.3.1 The Unified Modeling Language
4.1.3.2 The Object-Role Modeling Language
4.1.3.3 Compare and Combine
4.2 How to Develop a Conceptual Data Model
4.2.1 A Conceptual Schema Design Procedure
4.2.2 Top-Down and Bottom-Up Approaches
4.2.2.1 Dance and Conceptual Data Models
4.3 Limitations
References
5 Ontologies and Similar Artefacts
5.1 What Is an Ontology, the Artefact?
5.1.1 Syntax and Semantics
5.1.2 Automated Reasoning
5.1.3 An Ontology Is More Than Just a Logical Theory
5.2 Success Stories of Using Ontologies
5.2.1 Data Integration With the Gene Ontology
5.2.2 Outperforming the Scientists and Engineers
5.2.3 Automatic Question Generation and Marking with Ontologies
5.2.4 Ontologies as the Panacea?
5.3 Methodologies for Developing Ontologies
5.3.1 Bottom-Up Approaches to Ontology Development
5.3.2 Top-Down Approaches to Ontology Development
5.3.3 A Dance Ontology
5.4 Limitations
References
6 Ontology—With a Capital O
6.1 The Greeks and Then Some
6.2 Examples: Parthood and Stuff
6.2.1 Revisiting UML's Aggregation Association and GO'sPart-of
6.2.2 What's Lemonade, Really?
6.3 How to Do an Ontological Investigation
6.3.1 A Tentative Procedure
6.3.2 The Ontology of Dance
6.4 Limitations
References
7 Fit For Purpose
7.1 A Beauty Contest
7.1.1 A Feature-Based Comparison of the Types of Models
7.1.1.1 The Type of Model's Aims and Purposes
7.1.1.2 Language Freedom and Precision
7.1.1.3 Software Assistance and Development Methodologies
7.1.2 Comparison by Example
7.1.2.1 Comparing the Models About Dance
7.1.2.2 A Task-Based Comparison: Learning About Migrant Labour
7.2 Ethics and Modelling
7.2.1 Professional Behaviour and Practices
7.2.2 A Model's Features and Modelling Pitfalls
7.2.2.1 Property Manipulations
7.2.2.2 Aggregation or Granularity
7.3 Design Your Own Modelling Language
References
8 Go Forth and Model
Reference
Index