Conceptual models are descriptions of our ideas about a problem, used to shape the implementation of a solution to it. Everyone who builds complex information systems uses such models - be they requirements analysts, knowledge modellers or software designers - but understanding of the pragmatics of model design tends to be informal and parochial. Lightweight uses of logic can add precision without destroying the intuitions we use to interpret our descriptions. Computing with logic allows us to make use of this precision in providing automated support tools. Modern information scientists need to know what these methods are for and may need to build their own. This book gives you a place to begin. Where do you start when building models in a precise language like logic? One way is by following standard paradigms for design and adapting these to your needs. Some of these come from an analysis of existing informal notations. Others are from within logic itself. We take you through a sample of these, from more commonplace styles of formal modelling to non-standard methods such as techniques editing and argumentation. Each of these provides a window onto broader areas of applied logic and gives you a basis for adapting the method to your own needs.

Table of Contents:
Foreword Preface How to read this book Acknowledgements Chapter 1 Introduction 1.1 A dream 1.2 Reality 1.3 Our corner of the problem 1.4 What you can learn from this book 1.5 The way we view conceptual modelling 1.5.1 Models of problems are not specifications of solutions 1.5.2 What we require of conceptual models 1.5.3 Lightweight use of formality 1.5.4 Our use of logic 1.5.5 The need for eclecticism Chapter 2 Models in a design lifecycle 2.1 Requirements analysis 2.2 Choice of representational paradigms 2.2.1 Communication between divisions 2.2.2 Decision procedures within divisions 2.3 Model construction 2.3.1 Communication between divisions 2.3.2 Decision procedures within divisions 2.4 Validation and verification 2.4.1 Validation by analysing potential behaviours 2.4.2 Using our requirements to justify design decisions 2.5 Issues raised by our example 2.5.1 Argumentation networks 2.5.2 More extensive examples using operator models 2.5.3 Conceptual models in knowledge engineering 2.5.4 Model checking Exercises Chapter 3 Logic as a modelling language 3.1 Logics as frameworks for argument 3.2 The boundary problem 3.3 The search problem 3.4 Proof strategies 3.5 Describing proof strategies formally 3.6 Distinguishing proof rules from selection strategies 3.7 Knowing when two terms unify 3.8 The closed world assumption 3.9 Non-deductive patterns of inference 3.9.1 Abduction 3.9.2 Induction 3.10 Ontologies 3.11 Some properties of logical languages 3.11.1 Logical consequence 3.11.2 Correctness and completeness 3.11.3 Decidability 3.11.4 Correctness and completeness of arguments 3.12 Further reading Exercises Chapter 4 Communication 4.1 From domains to formal languages 4.1.1 An entity relationship diagram 4.1.2 A BSDM entity diagram 4.1.3 Lessons from the comparative analysis 4.1.4 Building an early model 4.1.5 Checking the consistency of the model 4.1.6 Lessons learned from more detailed modelling 4.2 From formal languages to domains 4.2.1 Visual formal expressions 4.2.2 Formal problem description by means of diagrams 4.3 The correspondence between logic and diagrams Exercises Chapter 5 Re-use of paradigms: parameterisable components 5.1 Worldwide web site generation 5.1.1 Problem description language (research group) 5.1.2 Parameterisable components (site pages) 5.1.3 Parameterisation system (simple instantiation) 5.1.4 Design generated (web site) 5.2 Rapid domain-specific model generation 5.2.1 Design generated (animal population model) 5.2.2 Parameterisable components (model fragments) 5.2.3 Problem description language (ecological conditions) 5.2.4 Parameterisation system (constrained generation) 5.3 Design endorsements 5.3.1 Problem description language (domain notations) 5.3.2 Design generated (shutdown specification) 5.3.3 Parameterisable components (shutdown segments) 5.3.4 Parameterisation system (design endorsement) 5.4 Costs and benefits 5.5 Further reading Exercises Chapter 6 Design processes inspired by formal methods 6.1 Constructing definitions by slices 6.1.1 Skeletons and additions 6.1.2 A techniques editor at work 6.2 Re-using part of an earlier definition 6.3 Using design histories when combining definitions 6.4 Issues raised by our example 6.4.1 Structured formal definition and transformation 6.4.2 Stripping structure from predicates 6.4.3 Case-based reasoning Exercises Chapter 7 Argumentation 7.1 Reasoning about sources of uncertainty 7.1.1 Reconstructing the model in logic 7.1.2 Reas