A Database Model for Knowledge Base Repository of Web and Mobile Application Expert System

Transcription

1 A Database Model for Knowledge Base Repository of Web and Mobile Application Expert System Istiadi 1, Emma Budi Sulistiarini 2 and Rudy Joegijantoro 3 1 Department of Electrical Engineering, Universitas Widyagama Malang, Indonesia, istiadi@widyagama.ac.id 2 Department of Industrial Engineering, Universitas Widyagama Malang, Indonesia, emma_budi@widyagama.ac.id 3 Department of Environmental Health Science, Widyagama Husada College of Health Malang, Indonesia, aziro71@gmail.com Abstract: Many problems associated with people or commmunity who need support solutions. Expert system is one potential approach to providing support solutions through consulting services. In a wide-scale expert system allows accessed over the internet (web) and mobile devices. But generally the expert system is only used in a domain problem. The main part of the expert system is a knowledge base, so for a variety of problem domains required a kind of repository. This paper aims to develop a database model as a repository of knowledge base for online expert system based on web and android application. A database design is proposed to organize knowledge bases in the form of the decision trees. The organization stated in a hierarchical structure. With this structure the user can direct the choice on the topic of certain consulting and application consulting will work in accordance with the selection. Keywords: Online Expert System, Decision Tree, Database, Repository Reference to this paper should be made as follows: Author(s) (2006) paper title, Int. J. Ad Hoc and Ubiquitous Computing, Vol. X, No. Y4, pp Biographical notes: approximately 100 words per author 1. Introduction Many problems related to the public or communities that need a solution. Such as the problems in identifying infectious disease that can cause loss of life; the problem in recognizing the livestock or crops diseases that cause a loss; as well as the problem in recognize and deal with problems of working equipment such as a computer trouble that can inhibit productivity and so on. One approach is by providing online consultation services that can utilize web media or mobile devices (smartphones). This approach can be performed using the expert system so that people can do interactive consultations using knowledge-based systems (Istiadi et al, 2014). The existence of the variety of access services, it provides the advantage with more affordable through the communications media (Seng-hui et al., 2008). With the popular communication medium for the public such as the Internet and other communication devices, the expert system allows distributed and deployed as knowledge (Duan et al., 2005). Thus the expert system that is more accessible to the community not only provide support for the solution but also can be used as a learning medium. Generally, the expert system was developed for a specific problem domain, but the communities have often encountered a variety of problems. Thus it takes an

2 expert system platform that accommodates a multiple of domain problems in the online services. In this case required a kind of repository to accommodate the knowledge bases for different problems, so that users can consult as needed. The knowledge base is a representation of knowledge from experts in specific fields (Turban and Aronson, 2005). One method is to represent it with a decision tree (Istiadi and Sulistiarini, 2013, and Wangphanich, 2011). The decision tree is a collection of nodes that have relationships, in which a node as a starting point for the process of identification of symptoms connected with nodes that will be selected according to the following facts that occurred. the identification process continues for the other node to node until it reaches the leaf node as a conclusion. The use of the decision tree representation could be used in expert systems online using the web and android applications (Istiadi et al, 2016). Representation of the decision tree is generally applied to a problem domain. When there are a number of decision tree with different domains, it would require some kind of repository to accommodate the representation. This is expected to increase the usability of the application service, since users need it for various problems. Development of a repository aims to provide a centralized container for objects that are shareable in a particular context, such as in some previous studies. Vitharana et al (2003) proposed a scheme and knowledge-base repository for the business component in the software field. The repository is designed to support users and managers in selecting the components that meet their requirements and provide aid in the assembly to determine the most compatible components in the deployed environment. Doer et al. (2010) developed a repository for 3D digital objects to support researchers in developing products and the dissemination to widely people. This repository is an integration of conceptual scheme and features of the objects. Perez-Perez et al (2012) developed a ModeleR, an environmental repository for managing models for experts that can be shared and developed by other experts. This repository is created based on an ontology that provides metadata system to document the process of model development. This study proposes a development of a repository model to accommodate the knowledge base for online consultation services. Representation of knowledge base referred to the decision tree approach. This is a continuation of our previous studies, which has proposed consulting services online using web and android applications (Istiadi et al, 2016). In this study, the model database as a container for representation of the knowledge base in previous studies will be expanded to be a repository, so that cases are accommodated more diversified and organized. 2. Proposed Model An expert system is a program that works on the basis of expert knowledge mapping (Turban and Aronson, 2005). In the wide scale, the expert system is a service that can be accessed through a network both on the fixed and mobile devices. These services work utilizing knowledge representation stored on the server through a remote system, so that it needed support systems to meet those requirements. a. Proposes System architecture The proposed system consists of a server as a host for the repository of Knowlege base in the database and applications program to serve the users (Figure 1). These applications are categorized into two types, namely development application and

3 consulting application (Turban and Aronson, 2005). Development Application is used for managing the knowledge base by the knowledge engineer, while the consulting application is used for user consultation. Figure 1. Proposed system architecture web-based consulting applications Knowledge Engineer Development Application Database of Knowledge Base Repository Internet (web) User Web Services for Android based consulting applications Android App. The consulting application on the server side is divided into two parts, are the consulting application for web-based services for Internet users and web services that provide data access to the android based consulting applications for users with smartphones. Both of these applications refer to the knowledge base repository that is stored in the database. As a repository that holds the knowledge base for a variety of cases, the database should be designed to meet the necessary requirements. The database shall be expressed in a particular data structure that allows for organizing various representation of cases. In this case, knowledge representation using a decision tree approach. b. Organizing the decision tree of the knowledge base This study utilizes decision tree approach to represent knowledge base. Figure 2 illustrates that decision tree consist set of nodes that are linked, which are devide as root node, decision node, and leaf node. Root node role as initial identification when the tracing process begin. This identification will go to next step into other nodes. This branching will lead to either decision node or leaf node. Decision node plays an important role that provide further branching to nodes. Leaf node acts to tag an end of a tree, it function as a conclusion point for certain solution.

4 Figure 2. Example of Decision Tree (Gamberger and Šmuc, 2016) When knowledge base of a problem domain is represented by a decision tree as the object of knowledge, it obtained a variety of decision trees for different problem domains. The diversity of the problem domain certainly has separate scopes that need to be organized. Organizing can be arranged in a hierarchical classification from broad scope to narrow scope (Istiadi et al, 2012). In the narrowest scope, contained objects of knowledge represented in the decision tree for specific cases. Thus, to accommodate diverse problem domains required organizing on it as illustrated in Figure 3. Figure 3. The concept of organizing knowledge bases Knowledge Domain level Knowledge Object level In accordance with the Figure 3, to accommodate the diversity of representation problem domain is necessary the presence of an organizing layer. This layer represents the hierarchical structure of the scope of the problem domains expert system. Apart from functioning domains surrounding issues, the structure can direct the user to choose the desired consultation case. c. Database model of knowledge base repository In order to provide knowledge base, which is accessible by applications, the decision tree model must be mapped into a database model. Based on decision tree concepts, there are four elements that is important to be accommodated in a particular database including problem domain definition, facts or symptoms definition, possible conclusion (diagnose) definition, and rule structure representation that illustrates the relationships between each nodes. Figure 3 alleges the database design for storing knowledge base using decision tree approach.

5 Figure 3. Database model of knowledge base repository Many tables in the database design shown above have functions as follows, Domain Caterory Table is used for represent a structure of organizing knowledge bases; Case Table provides options of a particular problem in a scope of domain catergory; Symptom Table has role to definition of facts as symptoms that possible raise; Diagnose Table accommodates in accordance with conclusions and corresponding suggestions; Rule Table represents decision tree approach as rule base. In the domain category table, there is a field domains title that marks the scope of a problem. In order to form an organizing structure, a Partof field is raised to connect across domains. This field is used for build a multilevel structure from broad scope to narrow scope. Thus, the hierarchical structure of the problem domain is possible to be formed. In the rule table, some fields are created as approach in order to implement decision tree. Those fields are IsRootNode field that handle data that indicate is root node or decision node; Identification_question field that stores data corresponding with symptoms question; Answer_Description fields which hold choice of label data as user answer; Answer_Link field that stores link to the related answers, it is a link to a decision node (next question) or a leaf node (conslusion); and IsAnswerLeafNode field that hold answer type, it is indicate the process will continue or finish. 3. Results and Discussion The system design is implemented as a prototype. Database as data storage of knowledge bases applied using MySQL. Web server for the Web applications and Web Services for data access of the Android applications utilize PHP. Android application uses the JSON format that is generated from a database repository. To test the use of the repositry, the database is filled with the category field of health and engineering domains. For the field of health is given the case of

6 tropical diseases such as dengue, malaria, chikungunya and ascariasis. While for the field of engineering illustrated by the case of troubleshooting on a computer that is referenced from Rosenthal (2008). Testing will be exemplified for a dengue cases were referred from the representation in the decision tree in Istiadi et al (2016). The tests carried out on web applications and android applications. a. Web based expert system testing In this case, the user can start the application by selecting one of the cases from the domain categories offered (Figure 4a). For example, the user selects cases of dengue fever in the health domain, the consultation process will begin with the identification of initial symptoms (Figure 4b). The identification process will run step by step to achieve a diagnosis result (conclusion) with an advice (Figure 4c). Figure 4. View of the web based expert system a. Case selection b. Initial symptom identification c. Conclusion (a) (b) (c) Based on these tests, the web application able to provide options for users that are loaded from the repository. Once the user selects one of the cases, the consultation process begins with the identification page initial symptoms as an indicator of the root node. The identification process ended to obtain a conclusion that indicates the position of the leaf nodes. b. Android base expert system testing This android based application that allows users to consult using mobile devices. Connection to the Internet is required to access the knowledge base through web services that are provided on the server. When a user starts the application, then a list of domain categories with cases offered will be loaded from the server. Users can choose one of them and android will be accessing the repository via web services to load the knowledge base according to user choice (Figure 5a). Once the knowledge base is loaded, then the consultation process can begin. Questions identification of initial symptoms would appear to be answered by the user (Figure 5b). The process will continue until a conclusion is obtained that also contains a given suggestions (Figure 5c).

7 Figure 5. View of the android based expert system a. Case selection b. Initial symptom identification c. Conclusion (a) (b) (c) In accordance with the above results it can be seen that the repository that holds the Knowledge base can be accessed by android application. Knowledge base from the a case can be loaded into the application, so that the consultation process can run on mobile devices. Applications have been able to carry out the consultation process from the identifying initial symptoms until it reaches a conclusion. 4. Conclusion The concept of organizing knowledge base are arranged in a hierarchy allows stored inside the database that can form a repository. Domain category table in the database berfunsi for structuring. Field partof can serve to linking betweendomains, so it can establish hierarchical structure. With this structure, the application can be directed to the knowledge base in case the user wants. This is evidenced by the performance from the both web and android application that can run the consultation process in accordance with the case. Acknowledgements We are grateful to the Ministry of Research Technology and Higher Education, which has funded this research through competitive research grant program in Reference Doerr, M., Tzompanaki, K., Theodoridou, M., Georgis, C., Axaridou, A., & Havemann, S. (2010, September). A Repository for 3D Model Production and Interpretation in Culture and Beyond. In VAST (Vol. 2010, p. 11th).

8 Duan, Y., Edwards, J. S., & Xu, M. X. (2005). Web-based expert systems: benefits and challenges. Information & Management, 42(6), Gamberger, D. & Šmuc, T., (2001) Decision Tree, Rudjer Boskovic Institute, Laboratory for Information Systems, Croatia (Accessed 15 September 2016) Istiadi, L. E. Nugroho, T. B. Adji, (2012) Model Ontologi Representasi Pengetahuan untuk Pengorganisasian Sumber Daya Pengetahuan, Conference on Information Technology and Electrical Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia, July 12, 2012 Istiadi and E. B. Sulistiarini, (2013) Representing Knowledge Base into Database for WAP and Web-based Expert System, International Conference on Information Systems for Business Competitiveness (ICISBC 2013), Semarang, Indonesia, December 5, 2013 Istiadi, I., Rofii, F., Qustoniah, A., Marisa, F., & Putra, G. D. (2016). An Online Expert System For Consultation Services Using A Mobile Application. Jurnal Teknologi, 78(6-3). Pérez-Pérez, R., Benito, B. M., & Bonet, F. J. (2012). ModeleR: An enviromental model repository as knowledge base for experts. Expert Systems with Applications, 39(9), Rosenthal, M. (2008). The Laptop Repair Workbook: An Introduction to Troubleshooting and Repairing Laptop Computers. Foner Books. Sheng-hui, D., Ying, G., & Xue-qin, Z. (2008, May). Design of the Intelligent Real- Time Hypertensive Diagnosis Expert System Based on Web. InInformation Processing (ISIP), 2008 International Symposiums on (pp ). IEEE. Turban, E., Aronson, J., & Liang, T. P. (2005). Decision Support Systems and Intelligent Systems 7 Edition ). Pearson Prentice Hall. Vitharana, P., Zahedi, F. M., & Jain, H. (2003). Knowledge-based repository scheme for storing and retrieving business components: a theoretical design and an empirical analysis. IEEE transactions on Software Engineering,29(7), Wangphanich, P. (2011, June). A simple Web-Based Expert System for a supplier assessment: A case of a JIT production environments. InProceedings 2011 International Conference on System Science and Engineering (pp ). IEEE.