Ontology-Based Document Management System for Public Institutions

2014 ◽  
pp. 234-254
Author(s):  
Aurelia Pătraşcu ◽  
Ana Tănăsescu ◽  
Constanţa-Nicoleta Bodea ◽  
Patricia Ordoñez de Pablos
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Open Source ◽  
Management System ◽  
Public Institutions ◽  
Town Planning ◽  
Document Management ◽  
Application Server ◽  
Expert System Shell ◽  
Document Management System

This chapter presents an ontology-based document management system developed for the Romanian public institutions. The system meets both general and specific requirements for this type of organization. The system has a three-tier architecture. FileZilla ftp server version 0.9.37 was used as application server. Jess Expert System Shell version 7.0p1 was the solution in developing knowledge base of the system and MySQL open-source server, version 5.0.51 is chosen for data tier. The system ontology is developed using the Protégé environment. The system is validated and deployed at Ploiesti City Hall. Employees from different departments (town planning, taxes etc.) working with the system provided validation information.

Expert System Based on ESTA and Guide for Condition Evaluation of Transformers

2011 ◽  
Vol 347-353 ◽  
pp. 306-309
Author(s):  
Qi Ming Cui ◽  
Shu Ting Cui ◽  
Zu Yuan Guan ◽  
Wen Tao Sun
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Power Transformers ◽  
Basic Process ◽  
State Variables ◽  
Application Research ◽  
Expert System Shell ◽  
Operation And Maintenance ◽  
Condition Evaluation ◽  
Maintenance Personnel

Through the application research of expert system shell ESTA, We have established a transformer condition evaluation expert system(ES) based on ESTA and corporate standards of State Grid (Q/GDW 169-2008) «Guide forCondition Evaluation of Oil-immersed Power Transformers (Reactors) ». Knowledge Base consists of several sections, parameters, rules in the evaluation of state variables, components and transformers. The value of the transformer condition evaluation expert system is that it supports and enhances the operation and maintenance personnel work, guidance (less experienced) the operation and maintenance personnel to understand the basic process of transformer condition evaluation. The application show that the system is effective and practical.

Expert Systems for the Prediction of Ovulation: Comparison of an Expert System Shell (Expertech Xi Plus) with a Program Written in a Traditional Language (BASIC)

1990 ◽  
Vol 29 (02) ◽  
pp. 140-145 ◽  
Author(s):  
A. Schreiner ◽  
T. Chard
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Diagnostic Accuracy ◽  
Knowledge Base ◽  
Ease Of Use ◽  
User Friendliness ◽  
Patient Response ◽  
Memory Space ◽  
Computer Diagnosis ◽  
Expert System Shell

AbstractThe use of an expert system shell (EXPERTECH Xi Plus) in the construction of an expert system for the diagnosis of infertility has been evaluated. A module was devised for predicting ovulation from the medical history alone. Two versions of this system were constructed, one using the expert system shell, and the other using QuickBASIC. The two systems have been compared with respect to: (1) ease of construction; (2) ease of knowledge base update; (3) help and explanation facilities; (4) diagnostic accuracy; (5) acceptability to patients and clinicians; (6) user-friendliness and ease of use; (7) use of memory space; and (8) run time. The responses of patients and clinicians were evaluated by questionnaires. The predictions made by the computer systems were compared to the conclusions reached by clinicians and to the “gold standard” of day 21 progesterone.The conclusions of this pilot study are: (1) the construction of this expert system was NOT facilitated by the use of this expert system shell; (2) update of the knowledge base was not facilitated either; (3) the expert system shell offered built-in help and explanation facilities, but as the system increased in complexity these became less useful; (4) after initial adjustment of decision thresholds the diagnostic accuracy of the system equalled that of the clinician; (5) the patient response to computer history-taking was very favorable but much less favorable to computer diagnosis; (6) the clinicians took a positive attitude to computer diagnosis; (7) the systems were easy to use; (8) the expert systems shell required much more memory space and had a much slower response time than the system written in BASIC.

An expert system for real-time well field management

2012 ◽  
Vol 12 (5) ◽  
pp. 699-706 ◽  
Author(s):  
B. S. Marti ◽  
G. Bauser ◽  
F. Stauffer ◽  
U. Kuhlmann ◽  
H.-P. Kaiser ◽  
...  
Keyword(s):  
Drinking Water ◽  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
Urban Areas ◽  
Industrial Sites ◽  
Water Wells ◽  
Well Field ◽  
Field Management ◽  
Historical Management

Well field management in urban areas faces challenges such as pollution from old waste deposits and former industrial sites, pollution from chemical accidents along transport lines or in industry, or diffuse pollution from leaking sewers. One possibility to protect the drinking water of a well field is the maintenance of a hydraulic barrier between the potentially polluted and the clean water. An example is the Hardhof well field in Zurich, Switzerland. This paper presents the methodology for a simple and fast expert system (ES), applies it to the Hardhof well field, and compares its performance to the historical management method of the Hardhof well field. Although the ES is quite simplistic it considerably improves the water quality in the drinking water wells. The ES knowledge base is crucial for successful management application. Therefore, a periodic update of the knowledge base is suggested for the real-time application of the ES.

Creating a Knowledge Base for a Mivar Expert System for the Diagnosis of Diabetes Mellitus

2021 ◽  
Vol 54 (6) ◽  
pp. 421-424
Author(s):  
H. Kim ◽  
D. A. Chuvikov ◽  
D. V. Aladin ◽  
O. O. Varlamov ◽  
L. E. Adamova ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Expert System ◽  
Knowledge Base ◽  
Diagnosis Of Diabetes Mellitus

scholarly journals Diagnostics and Prognostics of Energy Conversion Processes via Knowledge-Based Systems

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 1
Author(s):  
Roberto Melli ◽  
Enrico Sciubba
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
System Analysis ◽  
Intelligent System ◽  
Time Derivative ◽  
Time History ◽  
Control Procedure ◽  
Time Data ◽  
Ongoing Research

This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. The expert system is implemented in the CLIPS environment and is denominated PROMISA as the acronym for Prognostic Module for Intelligent System Analysis. It generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from the available real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator (Domain Expert), proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator over a pre-assigned interval of time. Whenever the value of this ‘‘discrete time derivative’’ becomes ‘‘high’’ with respect to a specified limit value, a ‘‘latent creeping fault’’ condition is prognosticated. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered—the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISA about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some simulation tests.

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