Knowledge organisation by means of concept process mapping Knowledge organisation by means of concept-process mapping
Complementary approaches to concept mapping as part of mixed-methods research: the role of Leximancer fuzzy concept mapping
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- What is the contribution of personal information management systems PIMS to the working model and personal work system of knowledge workers”
- Leximancer “fuzzy” concept maps.
- Leximancer “fuzzy” concept maps
22.6Complementary approaches to concept mapping as part of mixed-methods research: the role of Leximancer fuzzy concept mappingThe author’s current research is at heart a multi-methodology – cf. (Avison, Wood-Harper, Vidgen and Wood, 1998) - mixed-methods and inherently exploratory approach to a research question which can be simplified to: “What is the contribution of personal information management systems PIMS to the working model and personal work system of knowledge workers?” Mixed methods research mainly refers to quantitative and qualitative research in differing mixes. For an introduction to the issues, see (Ågerfalk 2013). (Goldkuhl 1995) presents a Habermasian view of information and action which is in contrast both to pragmatism as seen in (Ågerfalk 2010) and critical realism as seen in (Mingers et al. 2013) and (Zachariadis et al. 2013). Table indicates how two contrasting forms of concept mapping are used in experiments already underway or soon to be started. These two forms of concept mapping (which I believe to be complementary) are:
Concerning Leximancer: (Smith and Humphreys, 2006) report that the Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction—semantic and relational—using a different algorithm for each stage. “The Leximancer system performs a style of automatic content analysis. The system goes beyond keyword searching by discovering and extracting thesaurus-based concepts from the text data, with no requirement for a prior dictionary, although one can be used if desired. These concepts are then coded into the text, using the thesaurus as a classifier. The resulting asymmetric concept co-occurrence information is then used to generate a concept map.” (Smith and Humphreys, 2006). Thus what I term Leximancer “fuzzy” concept maps emerge from unsupervised (or, better in practice, semi-supervised) semantic mapping of natural language text. Figure shows the result of a semi-supervised Leximancer analysis of the author's PhD journal (circa 130,000 words): 
Figure Fuzzy concept map of the author's PhD journal produced using Leximancer Leximancer automatically recognises only single-word concepts. Most of my research concerns compound concepts; here is the list used when producing Figure 5: • information system (merge with IS) • personal information management system (merge with PIMS) • work system • personal work system (merge with PWS) • action research • knowledge management (merge with KM) • knowledge representation (merge with KR) • personal knowledge management (merge with PKM) • Personal Information Management (merge with PIM) Table also evidences the significance of concept mapping in this research work. Table Experiments planned or underway in the current research of Mark Gregory
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