Logico-linguistic modeling

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Logico-linguistic modeling is a method for building knowledge-based systems with a learning capability using Conceptual Models from Soft systems methodology, modal predicate logic and the Prolog artificial intelligence language.

Overview[edit]

Logico-linguistic modeling is a six stage method developed primarily for building knowledge-based systems (KBS), but it also has application in manual decision support systems and information source analysis. Logico-linguistic models have a superficial similarity to Sowa's[1] Conceptual Graphs, both use bubble style diagrams, both are concerned with concepts, both can be expressed in logic and both can be used in artificial intelligence. However, logico-linguistic models are very different in both logical form and in their method of construction.

Logico-linguistic modeling was developed in order to solve theoretical problems found in the Soft Systems method for information system design. The main thrust of the research into has been to show how Soft Systems Methodology (SSM), a method of systems analysis, can be extended into artificial intelligence.

Background[edit]

SSM employs three modeling devices i.e. rich pictures, root definitions, and Conceptual Models of human activity systems. The root definitions and conceptual models are built by stakeholders themselves in an iterative debate organized by a facilitator. The strengths of this method lie, firstly, in its flexibility, the fact that it can address any problem situation, and, secondly, in the fact that the solution belongs to the people in the organization and is not imposed by an outside analyst.[2]

Information Requirements Analysis (IRA)[3] took the basic SSM method a stage further and showed showed how the Conceptual Models could be developed into a detailed information system design. IRA calls for the addition of two modeling devices: "Information Categories" which show the required information inputs and outputs from the activities identified in an expanded conceptual model; and the "Maltese Cross" a matrix which shows the inputs and outputs from the information categories and shows where new information processing procedures are required. A completed Maltese Cross is sufficient for the detailed design of a transaction processing system.

The initial impetus to the development of logico-linguistic modeling was a concern with the theoretical problem of how an information system can have a connection to the physical world.[4] This is a problem in both IRA and more established methods (such as SSADM) because none base their information system design on models of the physical world. IRA designs are based on a notional conceptual model and SSADM is based on models of the movement of documents.

The solution to these problems provided a formula that was not limited to the design of transaction processing systems but could be used for the design of KBS with learning capability.[5]

The Six Stages of logico-linguistic modeling[edit]

Fig 1. SSM Conceptual Model

The logico-linguistic modeling method comprises six stages.[5]

1. Systems Analysis[edit]

In the first stage logico-linguistic modeling uses SSM for systems analysis. This stage seeks to structure the problem in the client organization by identifying stakeholders, modelling organizational objectives and discussing possible solutions. At this stage it not assumed that a KBS will be a solution and logico-linguistic modeling often produces solutions that do not require a computerized KBS.

Expert systems tend to capture the expertise, of individuals in different organizations, on the same topic. By contrast a KBS, produced by logico-linguistic modeling, seeks to capture the expertise of individuals in the same organization on different topics. The emphasis is on the elicitation of organizational or group knowledge rather than individual experts. In logico-linguistic modeling the stakeholders become the experts.

The end point of this stage is an SSM style conceptual models such as figure 1.

2. Language Creation[edit]

Fig 2. Logico-linguistic Model

According to the theory behind logico-linguistic modeling the SSM conceptual model building process is a Wittgensteinian language-game in which the stakeholders build a language to describe the problem situation.[6] The logico-linguistic model expresses this language as a set of definitions, see figure 2.

3. Knowledge Elicitation[edit]

After the model of the language has been built putative knowledge about the real world can be added by the stakeholders. Traditional SSM conceptual models contain only one logical connective (a necessary condition). In order to represent causal sequences, “sufficient condition” and “necessary & sufficient conditions” are also required.[7] In logico-linguistic modeling this deficiency is remedied by two addition types of connective. The outcome of stage three is an empirical model, see figure 3.

4. Knowledge Representation[edit]

Fig 3. Empirical Model

Modal predicate logic (a combination of modal logic and predicate logic) is used as the formal method of knowledge representation. The connectives from the language model are logically true (indicated by the “L” modal operator) and connective added at the knowledge elicitation stage are possibility true (indicated by the “M” modal operator). Before proceeding to stage 5, the models are expressed in logical formulae.

5. Computer code[edit]

Formulae in predicate logic translate easily into the Prolog artificial intelligence language. The modality is expressed by two different types of Prolog rules. Rules taken from the language creation stage of model building process are treated as incorrigible. While rules from the knowledge elicitation stage are marked as hypothetical rules. The system is not confined to decision support but has a built in learning capability.

6. Verification[edit]

A knowledge based system built using this method verifies itself. Verification takes place when the KBS is used by the clients. It is an ongoing process that continues throughout the life of the system. If the stakeholder beliefs about the real world are mistaken this will be brought out by the addition of Prolog facts that conflict with the hypothetical rules. It operates in accordance to the classic principle of falsifiability found in the philosophy of science[8]

Applications[edit]

  • Knowledge-based computer systems

Logico-linguistic modeling has been used to produce fully operational computerized knowledge based systems, such as one for the management of diabetes patients in a hospital out-patients department.[9]

  • Manual decision support

In other projects the need to move into Prolog was considered unnecessary because the printed logico-linguistic models provided an easy to use guide to decision making. For example, a system for mortgage loan approval[10]

  • Information source analysis

In some cases a KBS could not be built because the organization did not have all the knowledge needed to support all their activities. In these cases logico-linguistic modeling showed shortcomings in the supply of information and where more was needed. For example, a planning department in a telecoms company[2]

Criticism[edit]

While logico-linguistic Modeling overcomes the problems, found in SSM, in the transition from conceptual model to computer code, it does so at the expense of making the stakeholder constructed models much more complex. It has been argued that the benefits of this complexity is questionable[11] and that this modeling method is much harder to use than other methods[12]

This contention has been born out by subsequent research. In an attempt to use logico-linguistic modeling to model buying decisions across twelve companies, the researcher needed to simplify the models and the modal elements were discarded.[13]

References[edit]

  1. ^ Sowa, John F. (1984), Conceptual Structures: Information Processing in Mind and Machine, Addison-Wesley, Reading, MA, USA.
  2. ^ a b Gregory, Frank Hutson and Lau, Sui Pong (1999) Logical Soft Systems Modelling for Information Source Analysis - The Case of Hong Kong Telecom, Journal of the Operational Research Society, vol. 50 (2).
  3. ^ Wilson, Brian Systems: Concepts, Methodologies and Applications, John Wiley & Sons Ltd. 1984, 1990. ISBN 0-471-92716-3
  4. ^ Gregory, Frank Hutson (1995) Mapping Information Systems onto the Real World. Working Paper Series No. WP95/01. Dept. of Information Systems, City University of Hong Kong.
  5. ^ a b Gregory, Frank Hutson (1993)SSM for Knowledge Elicitation & Representation Warwick Business School Research Paper No. 98 (ISSN 0265-5976). Later published as Soft Systems Models for Knowledge Elicitation and Representation in Journal of the Operational Research Society (1995) 46, 562-578.
  6. ^ Gregory, Frank Hutson (1992) SSM to Information Systems: A Wittengsteinian Approach. Warwick Business School Research Paper No. 65. With revisions and additions this paper was published in Journal of Information Systems (1993) 3, pp. 149–168.
  7. ^ Gregory, Frank Hutson (1992) Cause, Effect, Efficiency & Soft Systems Models. Warwick Business School Research Paper No. 42. Later published in Journal of the Operational Research Society (1993) 44 (4), pp 149-168
  8. ^ Gregory, Frank Hutson (1996) "The need for "Scientific" Information Systems" Proceedings of the Americas Conference on Information Systems, Aug 1996, Association for Information Systems, 1996. pp. 534-536.
  9. ^ Choi, Mei Yee Sarah (1997) Logico-linguistic Modelling for building a Diabetes Mellitus Patient Management Knowledge Based System. M.A. Dissertation, Department of Information Systems, City University of Hong Kong.
  10. ^ Lee, Kam Shing Clive (1997) The Development of a Knowledge Based System on Mortgage Loan Approval. M.A. Dissertation, Department of Information Systems, City University of Hong Kong.
  11. ^ Klein, J. H. (1994) Cognitive processes and operational research: a human information processing perspective. Journal of the Operational Research Society. Vol. 45, No. 8.
  12. ^ Klein, J. H. (1995) Over-simplistic cognitive science: A response. Journal of the Operational Research Society. Vol. 46, No. 4. pp. 275-6.
  13. ^ Nakswasdi, Suravut (2004) Logical Soft Systems for Modeling Industrial Machinery Buying Decisions in Thailand. Doctor of Business Administration thesis, University of South Australia.

Further reading[edit]