Logico-linguistic modeling
Encyclopedia
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.
(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 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.
Information Requirements Analysis (IRA) 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. 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.
. 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.
in which the stakeholders build a language to describe the problem situation. The logico-linguistic model expresses this language as a set of definitions, see figure 2.
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.
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.
found in the philosophy of science
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.
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
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
and that this modeling method is much harder to use than other methods
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.
Conceptual model
In the most general sense, a model is anything used in any way to represent anything else. Some models are physical objects, for instance, a toy model which may be assembled, and may even be made to work like the object it represents. They are used to help us know and understand the subject matter...
from Soft systems methodology, modal predicate logic and the Prolog artificial intelligence language.
Overview
Logico-linguistic modeling is a six stage method developed primarily for building knowledge-based systemsKnowledge-based systems
Knowledge based systems are artificial intelligent tools working in a narrow domain to provide intelligent decisions with justification. Knowledge is acquired and represented using various knowledge representation techniques rules, frames and scripts...
(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 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
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.Information Requirements Analysis (IRA) 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. 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.
The Six Stages of logico-linguistic modeling
The logico-linguistic modeling method comprises six stages.1. Systems Analysis
In the first stage logico-linguistic modeling uses SSM for systems analysisSystems analysis
Systems analysis is the study of sets of interacting entities, including computer systems analysis. This field is closely related to requirements analysis or operations research...
. 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
According to the theory behind logico-linguistic modeling the SSM conceptual model building process is a Wittgensteinian language-gameLanguage-game
A language-game is a philosophical concept developed by Ludwig Wittgenstein, referring to simple examples of language use and the actions into which the language is woven.- Description :...
in which the stakeholders build a language to describe the problem situation. The logico-linguistic model expresses this language as a set of definitions, see figure 2.
3. Knowledge Elicitation
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. 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
Modal predicate logic (a combination of modal logicModal logic
Modal logic is a type of formal logic that extends classical propositional and predicate logic to include operators expressing modality. Modals — words that express modalities — qualify a statement. For example, the statement "John is happy" might be qualified by saying that John is...
and predicate logic
Predicate logic
In mathematical logic, predicate logic is the generic term for symbolic formal systems like first-order logic, second-order logic, many-sorted logic or infinitary logic. This formal system is distinguished from other systems in that its formulae contain variables which can be quantified...
) 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
Formulae in predicate logic translate easily into the PrologProlog
Prolog is a general purpose logic programming language associated with artificial intelligence and computational linguistics.Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is declarative: the program logic is expressed in terms of...
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
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 falsifiabilityFalsifiability
Falsifiability or refutability of an assertion, hypothesis or theory is the logical possibility that it can be contradicted by an observation or the outcome of a physical experiment...
found in the philosophy of science
Applications
- 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.
- 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
- 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
Criticism
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 questionableand that this modeling method is much harder to use than other methods
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.
Further reading
- Gregory, Frank Hutson (1993) "A logical analysis of soft systems modelling: implications for information system design and knowledge based system design. PhD thesis, University of Warwick.