Knowledge organisation by means of concept process mapping Knowledge organisation by means of concept-process mapping
Towards an ontological evaluation of Conceprocity
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- 22.5Learning by enquiry: some parallels with Checkland’s LUMAS
22.4Towards an ontological evaluation of Conceprocity(Wand, 1996) holds that despite the availability of a large number of systems analysis methods and techniques there does not exist a general underlying foundation for this knowledge domain. The stance which Wand adopts is that an information system is a representation of another “real-world” system. This ontological stance borrows from the philosophy of Mario Bunge, and in particular his ontological formalism as presented in (Bunge, 1977, 1979). Wand sees an information system as a representation that enables us to obtain knowledge about a certain domain without having to observe it. Thus where the represented domain might be termed the real-world system, an information system is an artificial representation of that real-world system, as perceived by somebody, built to enable information processing functions. (Wand, 1996) therefore challenges me to re-engineer Conceprocity starting from a clear ontological stance. (Wand and Weber, 2002) set out a framework for research on conceptual modelling in connection with information systems which has four main components: Table Conceptual Modelling Framework Elements (based on (Wand and Weber, 2002))
Macgilchrist challenges me to a more formal knowledge representation approach both for my models and my meta-models; he suggests first order logic in the form of Horn clauses supported by a Prolog implementation – see http://www.coli.uni-saarland.de/projects/milca/courses/comsem/html/index.html. I am rejecting this possibility for the time being because first-order logic is inaccessible to most of the research volunteers that I might reasonably expect to be able to work with in the near future. For now, I will take a much more pragmatic stance. Conceprocity is what it is because I sought for and have built a pragmatic knowledge representation scheme based on typed concept maps. It is a means to an end. That end is to complete my Ph.D. in the shortest possible time scales and of course to the highest possible standard commensurate with those timescales. In addition to Yair Wand and Ron Weber, Michael Rosemann and his colleagues have done much over the years to clarify the foundations and applicability of conceptual modelling. See in particular (Rosemann and Green, 2002), (Green and Rosemann, 2000; Rosemann and Wyssusek, 2005; Recker and Rosemann, 2009; Recker, Rosemann, Green and Indulska, 2011). In much of this work, the assumption is made that the scientific realist ontology of the Argentinian philosopher Mario Bunge is the correct basis for work on conceptual modelling. This is perhaps open to challenge or at least more open discussion. Bunge is quite clear in his work that he is primarily concerned with real things, their properties and actual events (Bunge, 1977). Although I am deeply sympathetic to his stance, he himself more directly applies it to science and in particular physics than to the social sciences. In the latter, the critical realism of Roy Bhaskar and what Bill McKelvey identifies as Campbellian realism (McKelvey, 1999) may well prove to be a more appropriate basis if only because more acceptable within the social sciences in general and in the information systems discipline in particular. 22.5Learning by enquiry: some parallels with Checkland’s LUMASIt is clear that concept maps lack ontological truth value in so far as they are only ever artificial artefacts that present an observer’s point of view concerning what is. Although I myself reject the phenomenological stance which underlies the work of Peter Checkland, it is possible to combine an ontological realist stance with some degree of epistemological relativism. The individual knowledge worker and learner seeks to understand concepts, the way they relate to one to another and to events in accordance with whatever point of view they adopt. In Conceprocity, we have sought to provide tools which inter alia assist greater understanding and in particular learning about the situation which is being modelled. Figure is an early illustrative summary of some of the propositions that we are here putting forward and discussing. Figure Illustrative summary of some of our propositions
We would comment that this diagram illustrates an inner learning loop as the researcher engages with perceived reality in accordance with some research methodology. She or he learns in a problem-focussed way as (s)he uses methods in an applied methodology. Just as (Argyris, 2000) describes double loop learning in organisations, we suggest that there potentially exists also an outer loop by means of which the researcher may learn at the more profound level described by Peter Checkland. (Checkland, 2000) presents (inter alia) LUMAS, Learning for a User by a Methodology-informed Approach to a problem Situation. Taking as his definition of methodology ‘a body of methods used in a particular activity’, Checkland suggests that a user knowledgeable about a methodology perceives a problem situation and uses the methodology to try to improve it. The methodology as a set of principles is converted by the methodology user into a specific method which the user feels to be appropriate for this particular situation at this moment in its history: “The user U, appreciating a methodology M as a coherent set of principles, and perceiving a problem situation S, asks himself (or herself): What can I do? He or she then tailors from M a specific approach, A, regarded as appropriate for S, and uses it to improve the situation. This generates learning L, which may both change U and his or her appreciations of the methodology: future versions of all the elements [of] LUMAS may be different as a result of each enactment of the process shown.” (Checkland, 2000)
Figure Checkland's LUMAS model Source: (Checkland, 2000) Checkland stresses that it is not the methodology which leads to improvement. It is the user as (s)he benefits from using the guidelines, as (s)he takes the formally defined methodology M to create or tailor A, the actual, user- and situation-specific approach adopted to the Real –world problem R that (s)he perceives a concern for. Asked recently in an interview by Frank Stowell (Stowell, 2013), whether he sees the “systems approach” as “a scientific methodology”; if so, how does it guide scientific inquiry, in your opinion? If not, how would you describe the relationship between a “systems approach” and scientific inquiry?” Peter Checkland has replied: “ As for the phrase ‘a systems approach’ I see it as being the name of any epistemology which encompasses the idea ‘system’; defined as the name of the concept of an adaptive whole which can adapt and survive in a changing environment. It thus has only epistemological, not ontological status. This is crucially important for the Systems Movement, this difference between Natural Science and so called ‘Social Science’. Thus, Marx has a theory of history, and his ideas change history, which is not law governed. On the other hand Copernicus and Galileo have different theories concerning whether our local universe is sun-centred or earth-centred; but these ideas can have no effect whatsoever on what is the case out there in the universe, which is law governed. What this means for a ‘systems approach’ is that if it engages with human and social phenomena it can develop only useful epistemology, not discover laws. ” (Stowell, 2013)4 Thus we suggest the existence of problem-focussed or situational learning – using methods in an applied methodology; and higher-level learning – which will manifest itself in a deepening appreciation of methodology and a concern to develop it further in action. We also suggest the possibility that the outer loop corresponds more-or-less directly to the inquiring / learning cycle of Checkland’s Soft Systems Methodology SSM. Directory: 2015 2015 -> 587 Return function, r i(X) r i(0) r i(1) r i(2) r i(3) 1 0 2 4 6 Thermal Station, I 2 0 1 5 6 3 0 3 5 6 10 2015 -> Fall 2013 Spring 2014 Program Data: Standard 1 Exhibit 4d 2015 -> Police Militarization 2015 -> Medical Radiography Program 2015 -> 2015 ihbb championships: hs history Bowl Round 4 – Prelims First Quarter 2015 -> Desk review yemen 2015 -> Wrtp/big step · 3841 W. Wisconsin Ave., Milwaukee, wi 53208 2015 -> 1. naclo 2015 – North American Computational Linguistics Olympiad 2015 -> Rao bulletin 1 December 2015 html edition this bulletin contains the following articles 2015 -> Volume Ⅴ (2015) On The 2015 China Victory Day Parade Download 395.15 Kb. Share with your friends:
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