Appendix-A: List of Agents Head Hand Knee Leg Foot Arm Finger Ankle Elbow Heel Lower Leg Wrist Toe Hip Shoulder Waist Back Torso Forearm Palm Pelvis Thigh Ball of Foot Chest Authors’ Information Balakrishnan Ramadoss – Department of Computer Applications, National Institute of Technology, Trichy 620015, India; e-mail: firstname.lastname@example.org.
Kannan Rajkumar – Department of Computer Applications, National Institute of Technology, Trichy 620015, India; e-mail: email@example.com.
AUTOMATED PROBLEM DOMAIN COGNITION PROCESS IN INFORMATION SYSTEMS DESIGN Maxim Loginov, Alexander Mikov Abstract: An automated cognitive approach for the design of Information Systems is presented. It is supposed to be used at the very beginning of the design process, between the stages of requirements determination and analysis, including the stage of analysis. In the context of the approach used either UML or ERD notations may be used for model representation. The approach provides the opportunity of using natural language text documents as a source of knowledge for automated problem domain model generation. It also simplifies the process of modelling by assisting the human user during the whole period of working upon the model (using UML or ERD notations).
Keywords: intellectual modeling technologies, information system development, structural analysis of loosely structured natural language documents.
ACM Classification Keywords: I.2 Artificial Intelligence: I.2.7 Natural Language Processing – Text analysis Fourth International Conference I.TECH 2006 Introduction The term “Problem domain” is usually used when the problem of Information Systems (IS) design is discussed.
This term represents the aggregation of knowledge about objects and active subjects, tied together with specific relations and pertaining to some common tasks.
Usually the scope of the problem domain is not described strictly and different problem domains intersect. Let us take two problem domains for example: a school education service and a public health service.
An information system designed for automating reporting at schools and another one designed for decisionmaking for health authorities of a city council can not be completely independent. There are medical consulting rooms at schools and the rate of sickness certainly depends on the school working conditions and so on. After all, both information systems share some personality information: many people are citizens and students at the same time.
Nevertheless, a description (a model) of the problem domain is a very important part of an information system project. But, anyway, if this model is not comprehensive then it is incomplete.
Documents and experts usually play a part of the knowledge sources circumscribing the problem domain. There are several types of documents that may be used: legal documents, ones that describe business processes, databases of employees and customers, etc. Human experts may provide information on informal rules, conventions, relative importance of concepts, etc, in the given problem domain. Documents of listed types denote objects and formalize some relations in the problem domain concerned. To a first approximation they may be considered as local models of these relations.
The difficulty is that most local models are built using different approaches, because there is no unified approach that may be applied to a problem domain (excepting some narrow-ranged technical domains, where local models can be combined together into a global model using some strict mathematical rules; information systems built upon such problem domains are called “systems of automatic control”).
We are concerned here about information systems of a different kind – systems where the human element is of primary importance. Investigation into such kinds of problem domains is a type of empirical research, related to the “sciences of the artificial”.
Nowadays most CASE tools (Computer-Aided Software Engineering tools) can automatically build source program code for a projected information system, using some initial formal model of the problem domain (usually the model is represented as a framework, or graph). The urgent problem is to automate the process of building the formal model, e. g. to automate the process of cognition in the given problem domain.
Goals of the Research The main purpose of this research is development of the special cognitive approach, referred to a class of Intellectual Modelling Technologies (IMT). This approach is designed for automating the process of information system development. Attention is focused on the very early stage of project development, the stage of analysis.
The problem domain of the class of IS under consideration includes a very large amount of legal documents (articles, assizes, bans, etc.), which regulate the status of objects, the behaviour of subjects related to an institution, etc. It also includes a settled system of document circulation. All this information, as a rule, is poorly structured. So, the development of a conceptual model of the problem domain (by means of UML language, for example) using knowledge from documents of these types, is a very difficult task and usually is done manually.
The suggested cognitive approach is aimed toward the problem of automating the conceptual-level model development by using loosely structured natural language documents.
Since the problem under consideration refers to a class of logical lexical systematization problems (as an example from the adjacent area of study we may take translation of natural language text into the language of Knowledge Engineering predicate logic), it has no solution using only a computation system. That is why the suggested approach is developed to work in conjunction with the human user. Human interference is needed during the automated analysis of problem domain described in source documents. Nevertheless, some self-learning capabilities in the context of approach allow us to depend on the self-development of the analyzer during persistent dialogue with the human user, so that subsequently it could be able to solve similar tasks without direct human assistance.
The suggested approach is oriented to be utilized at the earliest stage of the information system project development process. As indicated on fig. 1, the suggested approach is supposed to be used at the very beginning of the spiral loop, at the boundary between the stages of requirements determination and analysis, also including the stage of analysis.
Figure 1. The Spiral Model of Software Life Cycle It is important to mention that most existing IMT methods, used in CASE systems, automate, in general, stages of projecting, implementing, testing and integrating, but never touch the stages of requirements determination and analysis. Transition from the stage of requirements determination to the early stage of analysis is usually done by hand. Then the user develops a conceptual level model of the problem domain.
The model is developed usually using some special diagram language (UML language, for example). Conceptual level models describe a part of the real world for which the information system is being developed, so conceptual level class diagrams are right for describing the set of terms of the problem domain vocabulary.
When developing a model, the analyst usually processes by hand a large amount of documents referred to the problem domain in order to pick out key terms, properties, functions and relations between them. The proposed approach enables automation of this process. The intellectual cognitive analyzer being implemented according to the described approach should act as a user’s assistant. It will do the most routine part of the work in the early stage of analysis.
The suggested approach also includes some other capabilities that let the computer become quite a good assistant for the human user not just at the beginning of analysis, but along its whole length. One of those capabilities, in particular, is the automatic problem domain thesaurus building during interaction with the user.
And it is possible to use preinstalled thesauruses too, different ones for each problem domain, describing their specific components, features, etc.
Conceptual-level Modeling As was said earlier, the purpose of the approach is automated construction of conceptual-level model diagrams of the problem domain. The UML language (static class section), was chosen as a model representing language, because it is the most popular standard for CASE tools nowadays.
Fourth International Conference I.TECH 2006 UML static class diagrams define object classes and different kinds of static relations between them in the system. Also such things as class attributes, operations and relation limitations are usually shown on class diagrams.
There are three different points of view on how to interpret the sense of class diagrams:
- Conceptual-level point of view. If we take a class diagram from this point of view, then it reflects a set of terms and relations (called vocabulary) of the examined problem domain. Conceptual-level model considered independent from any software programming language.
- Specification-level point of view. In contrast to the above, this affects the software development range, but focuses attention over interfaces, not implementation. Looking at the class diagram from this point of view, designers have to do rather with types, not classes.
- Implementation-level point of view. In this case we really deal with graphical representation of the class structure of software. So the designer goes down to the level of implementation.
Understanding which point of view should be used and when, is extremely important either for developing or for reading class diagrams. Unfortunately, distinctions between them are not understood clearly, so the majority of developers often mix some different points of view when developing a diagram model.
The idea of the point of view on diagrams is not actually a formal part of UML language, but it is extremely important. UML constructions can be used with any of the three points of view in mind.
As has already been said, the suggested approach is going to be used for the automation of the process of conceptual-level problem domain model development. First of all, it is because of the fact that the approach should work at the most initial stage of IS development process. Apart from that, the nature of the documentation used in the problem domain of the considered range of IS (sphere of education) means that the description of objects and their mutual relations is of a sufficiently high level. This fact automatically determines the point of view on a problem domain as conceptual.
However, such a strict binding model to a conceptual level is not obligatory. In some cases the model can get an interpretation from some other point of view. This mainly depends on the nature of the source documents.
Conceptual-level diagrams describe the problem domain vocabulary. Of course, it is doubtful that diagrams developed using the suggested approach could be immediately used for generation of skeleton program code, but it can be used for subject domain database logic structure generation.
IES Architecture Fig. 2 shows the diagram reflecting the principle according to which the projected system is organized.
Let us consider in more detail the principles assumed for the basis of the suggested approach.
Natural language expresses relations between items in a problem domain in the form of statements. For example, the statement “children study at schools” binds together the concept “school” belonging to the class “educational institutions” and the concept “children” belonging to the class “person”. Any statement can be either correct, or wrong, when established during correlation with reality. So, statements singled out from source documents should be compared to the problem domain thesaurus which reflects the current actuality. In the case of detection of a discrepancy of the obtained propositions to ones from the problem domain thesaurus, the latter should be brought into accord with reality, or the source proposition should be corrected in an appropriate way. When the system cannot make such a decision independently, it can apply for the human user's assistance.
The proposition (statement) is an expression that claims or disclaims the existence of an item, the relation between an item and its attribute, or the relation between two items. A sentence is the language form of the proposition. Propositions in natural language texts are expressed by narrative sentences, for example:
Knowledge Engineering “institutions of primary vocational training may be state, municipal and private”. The proposition of connexion of an item and its attribute consists of propositional subject, and a predicate reflecting an attribute of an item. Except for subject and predicate, the proposition includes a copula which can be put into words (for example, “not is”, “is”, etc.).
Figure 2. IES Architecture Framework Depending on what is claimed or disclaimed in the proposition – either the existence of an item, or the relation between an item and its attribute, or the relation between two items – it may be classified as attributive proposition, proposition with relation and existential proposition. A proposition is called compound if it consists of several simple propositions, combined together in an expression.
A conceptual-level model is usually developed using source natural language description. Sentences in this description are propositions of listed types. Some of them concern certain objects; others are general as they concern some class of objects in the problem domain. Source documents consist of compound sentences that describe objects and relations between them in the problem domain.
In the course of linguistic analysis using knowledge of language structure, initial compound sentences are split into simple propositions of three listed types, and the type of each proposition can be determined during the process of decomposition.
The whole totality of concepts and relations between them, expressed by means of natural language, forms a system thesaurus. Thus, we can say it schematically represents the matter of the source documents text. The idea of a thesaurus is frequently applied to problems of semantic search in documents. Within the suggested approach, another variant of its application is offered.
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