Knowledge organisation by means of concept process mapping Knowledge organisation by means of concept-process mapping
Analytics based on Conceprocity models
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- 18.1A functional perspective: (Zeng, 2008)
- 20.1Some further evaluative comments on concept mapping
16.2Analytics based on Conceprocity modelsConceprocity models are stored in the Lucidchart server system. They can be saved in .vdx format, this being the Microsoft Visio XML format – see http://msdn.microsoft.com/en-us/library/office/aa218409%28v=office.10%29.aspx It is therefore potentially possible to: §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. §18Carry out various forms of data analysis, potentially including metrics such as Betweenness, Closeness, Diameter, Clustering Coefficient, Average shortest path… See https://gephi.org/ (but no direct XML import) and http://www.liquid-technologies.com/xml-schema-editor.aspx I do NOT anticipate carrying out such analysis in PhD timescales. 18.1A functional perspective: (Zeng, 2008)(Zeng, 2008) suggests a table of functional effectiveness reproduced as FIG 9. in (Rocha Souza, Tudhope and Barcellos Almeida, 2010) and below as Figure : 
Figure KOS Spectrum from (Zeng, 2008, p.161) with suggested functional effectiveness We now suggest a positioning of Conceprocity based on Figure – see Table following, which:
§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 §20extends it with additional rows: visualisation and suitability for machine processing Table A suggested positioning of Conceprocity and other KOS in Functional Effectiveness terms
20.1Some further evaluative comments on concept mapping(Trochim, 1989) introduces the use and usefulness of concept maps for planning and evaluation. The same author discusses their reliability in (Trochim, 1993). More recently he and colleagues have discussed concept mapping as an alternative approach for the analysis of open-ended survey responses (Jackson and Trochim, 2002). More speculatively, (Thimor and Fidelman, 1995) have suggested: “Concepts are related to logic. There are several approaches to logic and in every approach concepts are created and interrelated differently. There are two possibilities for the hierarchic set-up of hierarchic concept-maps: one possibility is a top-down map; the other is a bottom-up map. Therefore, individual differences in concept-mapping may be related to individual differences in the approach to logic. It was [earlier] suggested by Fidelman that different approaches to logic may be related to individual differences in the relative efficiencies of the hemispheric analytical and synthetical mechanisms. In the sequel a theory relating concept mapping to the cerebral hemispheres through logic is suggested. According to this theory, top-down concept-mapping is more related to the right hemisphere relative to bottom-up mapping. On the other hand, bottom-up mapping is more related to the left hemisphere relative to top-down mapping.” They go on to suggest a contrast between the formation of concepts in Bertrand Russell’s logic which they present as bottom-up, and on the other hand, in Gottlieb Frege’s logic concepts – where a top-down formation is primary: (Thimor and Fidelman, 1995). This suggests that concept mapping can appeal to different populations who will tend to use it in various complementary ways, particularly with the emergence of cloud-based approaches based on collaborative infrastructure. It is in part for this reason that we have introduced the notion of usage profiles into Conceprocity.
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