Knowledge organisation by means of concept process mapping Knowledge organisation by means of concept-process mapping
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Knowledge organisation by means of concept-process mapping Mark GREGORY www.TeamKIM.org §1Preface to this introductory paperConceprocity – concept <-> process reciprocity – is a visual and textual language and toolset intended for capturing, expressing, communicating and co-creating models of topic areas of domain knowledge by domain experts or learners. This introductory paper introduces, positions, compares and justifies Conceprocity. The paper positions Conceprocity within the existing spectrum of approaches to data and knowledge organisation. It also introduces some of the more immediate use cases for which it has been created. Specifically it discusses how Conceprocity is being used in research into personal work systems and in the teaching of business information systems and the principles of e-commerce. In this paper we position Conceprocity as a “knowledge organisation system”, cf. (Friedman and Smiraglia, 2013; Friedman and Thellefsen, 2011). This paper also begins to demonstrate by a process analogous to the “stepwise refinement” of the software engineer Nicklaus Wirth (Wirth, 1985), how a concept, procedure or principle at one level of hierarchy can be decomposed and expanded (“exploded”) by means of a sub-model. To the extent that a concept can be modelled on a computer as a data table or a software object, a procedure realised in software, and a principle implemented as an algorithm or a heuristic or as a set of constraints - the whole responding to events in the real world and in the computer: Conceprocity can also be used as a requirements definition and analysis language. However, the full description of how Conceprocity can and should be used for software requirements analysis and for package selection is left to a later paper, as yet unwritten. Readers of this document might like also to make reference to the online presentations of Conceprocity which can be found on the website www.MarkRogerGregory.net. In addition to a narrated audio presentation, these presentations include video demonstrating the actual construction of a Conceprocity model using Lucidchart. The presentations are of varying lengths, with the longest aimed for use by people who intend actually to learn how to use Conceprocity in their own work.
§1Preface to this introductory paper 1 §2An introduction to Conceprocity 3 2.1Why model personal knowledge conceptually? 3 2.2Why Conceprocity is important and indeed essential to my work 4 2.3Designing your working life: learning how to get things done better 4 2.4Example Conceprocity model 4 2.5How we can design a better personal work and personal information management system 5 2.6Explicit design and serendipitous bricolage 5 2.7Positioning Conceprocity as a Knowledge Organisation Representation 5 §3Conceprocity described and illustrated 6 3.1An example Conceprocity model and how it has been created 6 3.2Conceprocity as a modelling language 8 3.3Conceprocity: Notions 9 3.4Representing Conceprocity relationships 10 §4An introduction to conceptual modelling as knowledge representation within knowledge organisation systems 11 4.1Taxonomy, ontology, and knowledge representation 12 4.2Conceptual modelling for requirements analysis 13 4.3Conceptual modelling for simulation and execution 15 4.4Conceptual modelling for learning and understanding 15 4.5Conceptual modelling for knowledge representation 16 4.6Some problems in conceptual modelling 16 4.7Reading and writing conceptual models: different skills, different outcomes 16 §5An important aside: formal conceptual structures 16 §6The design of Conceprocity and its justification 16 6.1How and why Conceprocity differs from G-MOT 16 6.2G-MOT strengths 17 6.3Why Conceprocity distinguishes concepts, procedures and principles 18 §7Imprecise meaning of the links between the entities that compose the model. 18 §8The ambiguities in graphs where objects, actions on objects and statements of properties that those objects possess are all mixed up and are not represented in a way that helps to differentiate them and uncover their relationships. Paquette suggests distinguishing classes of objects as concepts, actions on concepts as procedures and statements of properties as principles. 18 §9The difficulty of combining in one model objects which at a high summary level in the model need to be developed at a lower level with sub-models whose nature is not the same. Thus for example a principle at a high level might need to be developed as a procedural or conceptual sub-model. 18 §10Existing visual representation formalisms have emerged largely from the computer science and software engineering communities. Formalisms such as Entity Relationship models, modern structured systems analysis, conceptual graphs (John Sowa (Sowa, 2000b, 1984) following Charles Pierce, the object modelling technique and the successor Unified Modelling Language UML are all representation approaches which have been built primarily for the analysis and architectural design of complex software systems. Even to read such diagrams and the links between them is hard, and to create such models requires considerable expertise and an abstraction and conceptualisation capability which may be lacking among the more general knowledge workers whom Paquette (and I) wish to address and empower. Paquette states: 18 10.1Main notions and the symbols used to represent them 18 10.2Additional notions in Conceprocity and why they have been added 19 10.3Conceprocity relationships 20 10.4Events and Logical Connectors 25 10.5Lists and properties 28 10.6A summary of Conceprocity grammar rules 28 10.7Some simple rules to follow 30 10.8Structuring Conceprocity maps 30 10.9Conceprocity Usage Profiles 31 10.10Learning to use Conceprocity: moving on from the beginner’s profile 31 10.11Conceprocity for the Right Brain 33 10.12Specific PhD research process as a Conceprocity concept map 33 §11The role of Conceprocity in the PhD research of Mark Gregory: some criticisms and the ways in which they are addressed in the research design 34 11.1Why Conceprocity is important in my PhD research 34 11.2The challenge according to David Weir 35 11.3My response to David Weir’s challenge 35 11.4Renaud Macgilchrist’s challenge 37 §12The infinite range of specific models, instances of models, which can be constructed using those language systems. 37 12.1My response to Renaud Macgilchrist’s challenge 37 12.2Recap: why are concept maps essential to this Ph.D. research? 38 §13Ways of organising personal knowledge and data 39 13.1Systems thinking and modelling 39 13.2A Wikipedia introduction to Knowledge Organisation 40 13.3Schema representation 40 13.4Knowledge Representation 41 13.5Personal Information Management System PIMS Data Structures 43 §14Documents “marked up” with semantic information (an extension of the HTML tags used in today’s Web pages to supply information for Web search engines using web crawlers). This could be machine-understandable information about the human-understandable content of the document (such as the creator, title, description, etc., of the document) or it could be purely metadata representing a set of facts (such as resources and services elsewhere in the site). (Note that anything that can be identified with a Uniform Resource Identifier (URI) can be described, so the semantic web can reason about animals, people, places, ideas, etc.) Semantic mark-up is often generated automatically, rather than manually. 46 §15Common metadata vocabularies (ontologies) and maps between vocabularies that allow document creators to know how to mark up their documents so that agents can use the information in the supplied metadata (so that Author in the sense of ‘the Author of the page’ won’t be confused with Author in the sense of a book that is the subject of a book review). 46 15.1Knowledge Organisation: an LIS (library and information science) perspective 47 §16Positioning Conceprocity among Knowledge Organisation Systems 48 16.1Knowledge Representation (KR) as the primary dimension for classifying and comparing Knowledge Organisation Systems KOS 48 16.2Analytics based on Conceprocity models 52 §17Create concept dictionaries and a database of Conceprocity terms and usages; these in turn can form the basis for semi-automatic ontological analysis and perhaps comparison of personal ontologies. 52 §18Carry out various forms of data analysis, potentially including metrics such as Betweenness, Closeness, Diameter, Clustering Coefficient, Average shortest path… 52 18.1A functional perspective: (Zeng, 2008) 52 §19extends it with additional columns both with two additional KOS which I here identify ([1]the semantic web and its RDF and OWL knowledge representation and [2] first-order logic) and also with Conceprocity 53 §20extends it with additional rows: visualisation and suitability for machine processing 53 20.1Some further evaluative comments on concept mapping 54 20.2Usage profiles 54 20.3Conceprocity conceptual data structures 56 §21Evidence for the usefulness of Conceprocity 58 21.1Research into the Working Model of knowledge workers 58 21.2Modelling the content of academic articles 59 21.3Action learning by students 59 21.4Consequent improvements to Conceprocity 59 21.5Information systems requirement analysis 59 21.6Modelling action-oriented knowledge 59 §22A critical evaluation of Conceprocity and some suggestions for future work 60 22.1The tentative nature of these initial conclusions: further research proposed 60 22.2Metrics 60 22.3More fundamental difficulties and objections 61 22.4Towards an ontological evaluation of Conceprocity 64 22.5Learning by enquiry: some parallels with Checkland’s LUMAS 66 22.6Complementary approaches to concept mapping as part of mixed-methods research: the role of Leximancer fuzzy concept mapping 70 §1Appendix: Introduction to the Conceprocity notation 74 1.1How to build a Conceprocity model 75 1.2TROPICPEA structural relationships 76 1.3Where to find out more concerning Conceprocity 78
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