Knowledge organisation by means of concept process mapping Knowledge organisation by means of concept-process mapping


Positioning Conceprocity as a Knowledge Organisation Representation



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2.7Positioning Conceprocity as a Knowledge Organisation Representation


Concepts may be held both visually and linguistically. Indeed, some hold that there exists right brain and left brain thinking. This notion has developed from the late-1960s research of psycho-biologist Roger W Sperry (see for example the popular presentation (Sperry, 1975)), who discovered that the human brain has two very different ways of thinking corresponding more-or-less to the two separate brain hemispheres):

  • Right brain thinking is visual and processes information in an intuitive and simultaneous way, looking first at the whole picture then the details.

  • Left brain thinking is verbal and processes information in an analytical and sequential way, looking first at the pieces then putting them together to get the whole.

There are various approaches that have been developed to represent knowledge in ways which explicitly exploit right-brain approaches or seek to stimulate right-brain reactions. These include:

  • Mind Maps –Tony Buzan (Buzan and Buzan, 1996)

  • Concept maps – Joseph Novak and collaborators (Novak and Cañas, 2008) following David Ausubel (Ausubel, 2000, 1963)

  • Concept maps with typed concepts and relationships: G-MOT from LICEF (Paquette, 2010); (Basque, 2013)

  • Concept ↔ Process maps: Conceprocity: Mark Gregory (www.markrogergregory.net)

Using both the visual and the linguistic (written and spoken language) stimulates better understanding of a situation and – perhaps later – better learning.

All these approaches are based on the active creation by individuals of specifically-crafted or designed maps of concepts and their relationships. An alternative and, we believe, complementary approach is that of “fuzzy” concept maps discussed below in section 22.6 and included in Table .


§3Conceprocity described and illustrated

3.1An example Conceprocity model and how it has been created


  • Start with a simple English sentence: “The cat sat on the mat”

    • Give a specific instance: “The cat called Kat sat on the mat in my lounge”

    • A concrete Conceprocity map follows:

Figure A concrete model: Kat sitting in Mark's lounge



  • Identify concepts, any static relationships and any activities

  • Create a specific and a more general model using the meta-concepts (Conceprocity calls them notions) of concept, procedure and relationship

  • Consider concrete and abstract representations

  • Observe, maybe discuss and then refine the resulting map

    • Here we choose to remove the concrete and retain the abstract elements in a conceptual model of the general situation of creatures acting in a geographical context

Figure More general Conceprocity map



  • The model that results depends upon the viewpoint and the purpose of the modeller – what (Checkland, 1981) identified as the Weltanschauung of this important participant

    • A cat specialist (and a cat lover!) will take a different view from an expert in cognitive science applied to animals

    • But the process of individual understanding than collective dialogue and of mutual understanding can be aided by visual concept mapping and by dialogue around the models

3.2Conceprocity as a modelling language


A modeller creates an initial model which is then refined (evolved and simplified)

    • Either alone by the original modeller

    • Or by means of co-modelling by the original modeller and other modellers who may be better “wielders” of Conceprocity

    • Or by exchange between the modeller(s) and domain experts who are not (yet) Conceprocity modellers

The aim is to create well-expressed models which enhance both the understanding of the phenomenon being modelled and the understanding and ongoing learning of the modeller(s).

It is essential to distinguish between Conceprocity as a modelling language, often used for co-modelling by persons more or less skilled in knowledge mapping; from the actual models produced in Conceprocity.


3.3Conceprocity: Notions


Paquette’s G-MOT and Conceprocity differ from the more usual concept maps of (Novak and Cañas, 2008) by distinguishing between types (classes) of objects:

    • Concepts (things, ideas, etc.; these are usable and (sometimes) decidable classes of knowledge)

    • Procedures (the means of enacting knowledge in the form of specific activities, repeatable actions and processes – the latter being templates for repeated actions)

    • Principles (rules, constraints, permissions; also computer programs – viewed as concrete expressions of algorithms and an encoding by programmers of knowledge)

    • Actors (people, organisations, external systems)

Conceprocity goes beyond G-MOT in a number of ways. In particular, it supports the notions of:

    • Events: events describe changes of condition or state; they typically characterise the result of an activity and in turn trigger the next activity

    • Logical connectors: OR XOR AND NOT

Conceprocity can therefore be used for modelling algorithms and heuristics.

See Figure Conceprocity representation of abstract notions and concrete facts.



Figure Conceprocity representation of abstract notions and concrete facts

These typed classes (e.g. cat) or instances of objects (e.g. Kat) are related by relationships or relationship instances (links) which are themselves also typed.

We contend that greater semantic precision permits more expressive and more meaningful models.




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