Developing Troubleshooting Systems Using Ontologies (original) (raw)
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Stored-Knowledge Based Troubleshooting and Diagnosing System
IJSES, 2018
Recognizing and detecting system problems by any human intelligent is a complicated process which demands high technical-know-how just as simple problem could take hours or even days to solve. Expert systems use human knowledge often stored as rules within the computer to solve problems that generally would entail human intelligence. This paper proposes a stored-knowledge based troubleshooting and diagnosing system that mimic human intelligence in solving system problems. The system is composed of a user interface, a knowledge-base, an inference engine, and a stored-knowledge based system interface including an intelligent agent that assists in the knowledge acquisition process. The stored-knowledge based system is meant to automate troubleshooting and diagnosis of system problems that ordinarily would have been carried out manually by human intelligence, which is laborious, costly, and time consuming. The developed system used backward chaining approach to infer the rules while the forward chaining approach is left as future work.
On the Usefulness of Different Expert Question Types for Fault Localization in Ontologies
Lecture Notes in Computer Science
When ontologies reach a certain size and complexity, faults such as inconsistencies or wrong entailments are hardly avoidable. Locating the faulty axioms that cause these faults is a hard and timeconsuming task. Addressing this issue, several techniques for semiautomatic fault localization in ontologies have been proposed. Often, these approaches involve a human expert who provides answers to system-generated questions about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as few and as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting user and the metric to be optimized. In this work, we critically discuss these optimization criteria and suppositions about the user. As a result, we suggest an alternative, arguably more realistic metric to measure the expert's effort and show that existing approaches do not achieve optimal efficiency in terms of this metric. Moreover, we detect that significant differences regarding user interaction costs arise if the assumptions made by existing works do not hold. As a remedy, we suggest a new notion of expert question that does not rely on any assumptions about the user's way of answering. Experiments on faulty real-world ontologies testify that the new querying method minimizes the necessary expert consultations in the majority of cases and reduces the computation time for the best next question by at least 80 % in all scenarios.
A New Expert Questioning Approach to More Efficient Fault Localization in Ontologies
ArXiv, 2019
When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for semi-automatic fault localization in ontologies have been proposed. Often, these approaches involve a human expert who provides answers to system-generated questions about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting user. In this work, we characterize and discuss different user types and show that existing approaches do not achieve optimal efficiency for all of them. As a remedy, we suggest a new type of expert question which aims at fitting the answe...
An interactive fault diagnosis expert system for a helpdesk application
Expert Systems, 1996
Abstract: This paper presents work on an interactive fault diagnosis expert system for a helpdesk application. A knowledge representation and inference algorithm is proposed to satisfy three design specifications: (1) no parallel event exists in human fault reporting; (2) the diagnostic sequence is unpredictable, and (3) the inference engine is passive in an event-driven environment. A lattice data structure is designed for knowledge representation, which is generated automatically from a script of decision rules. The inference engine works in a transaction-like style by prompting and responding to the user according to the knowledge in the lattice. It can explicitly guide the inference sequence, as well as respond to ad hoc input from the user.
Knowledge-based system support for the information center's troubleshooting function
Expert Systems With Applications, 1991
Knowledge-based systems support for the consulting~troubleshooting function within information centers was investigated. This function is defined and characteristics are identified which make it an appropriate target for knowledge-based systems support. A general architecture for a troubleshooting expert system was developed and its elements are described. A case is described which illustrates the application of an expert system in the provision of troubleshooting service by the information center of a major state university. The results illustrate the impact that such a knowledgebased system approach can have in the troubleshooting area, especially in relation to the efficient utilization of information center personnel.
2009 International Conference on Multimedia Computing and Systems, 2009
This paper describes the design of a fault diagnostic maintenance system for steam turbines based on a domain ontology of the turbine and coupled to an expert system. The main data and information constituting the system come from disparate data bases with different usage. In this case a database for equipment characteristics and another containing maintenance acts defining symptoms, defects and remedies for maintenance cases. An Expert System (ES) integrated with the domain ontology of the steam turbine is used as a reasoner in order to generate new knowledge. The philosophy of such an approach consists on one hand, in the capitalisation of all the knowledge gathered from both databases and integrated into a single ontology creating relationships between classes. On the other hand, the ES represent a powerful tool in order to exploit the ontological representation for aided diagnostic and maintenance. The final system must be independent from any ontology editor such as Protege, as well as a specific expert system shell that may limit its online use.
Use a domain ontology to develop knowledge intensive CBR systems for fault diagnosis
2012 International Conference on Information Technology and e-Services, 2012
The maintenance of industrial systems is crucial for productivity, products quality and supplied services. Numerous computer systems are therefore developed for the task and must, in most cases, collaborate with each other. In the light of this statement, our work aims at realizing a system which consists in gathering the knowledge and the know-how in the field of fault diagnosis for steam turbines, by the construction of domain ontology. In order to better exploit the ontology and reason using its classes, sub-classes and instances a Case Based Reasoning (CBR) paradigm is chosen, as it offers an ideal solution for diagnostic of real application systems. The paper, therefore, presents a current work which has for objective to develop a CBR application for fault diagnosis based on ontology, by using the API JColibri.
An ontology-based software agent system case study
Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing, 2003
Developing a knowledge-sharing capability across distributed heterogeneous data sources remains a significant challenge. Ontology-based approaches to this problem show promise by resolving heterogeneity, if the participating data owners agree to use a common ontology (i.e., a set of common attributes). Such common ontologies offer the capability to work with distributed data as if it were located in a central repository. This knowledge sharing may be achieved by determining the intersection of similar concepts from across various heterogeneous systems. However, if information is sought from a subset of the participating data sources, there may be concepts common to the subset that are not included in the full common ontology, and therefore are unavailable for knowledge sharing. One way to solve this problem is to construct a series of ontologies, one for each possible combination of data sources. In this way, no concepts are lost, but the number of possible subsets is prohibitively large. This paper describes a software agent case study that demonstrates a flexible and dynamic approach for the fusion of data across combinations of participating heterogeneous data sources to maximize knowledge sharing. The software agents generate the largest intersection of shared data across any selected subset of data sources. This ontology-based agent approach maximizes knowledge sharing by dynamically generating common ontologies over the data sources of interest. The approach was validated using data provided by five (disparate) national laboratories by defining a local ontology for each laboratory (i.e., data source). In this experiment, the ontologies are used to specify how to format the data using XML to make it suitable for query. Consequently, software agents are empowered to provide the ability to dynamically form local ontologies from the data sources. In this way, the cost of developing these ontologies is reduced while providing the broadest possible access to available data sources.
SELF LEARNING COMPUTER TROUBLESHOOTING EXPERT SYSTEM
In computer domain the professionals were limited in number but the numbers of institutions looking for computer professionals were high. The aim of this study is developing self learning expert system which is providing troubleshooting information about problems occurred in the computer system for the information and communication technology technicians and computer users to solve problems effectively and efficiently to utilize computer and computer related resources. Domain knowledge was acquired using semi-structured interview technique, observation and document analysis. Domain experts were purposively selected for the interview question. The conceptual model of the expert system was designed by using a decision tree structure which is easy to understand and interpret the causes involved in computer troubleshooting. Based on the conceptual model, the expert system was developed by using 'if – then' rules. The developed system used backward chaining to infer the rules and provide appropriate recommendations. According to the system evaluators 83.6% of the users were satisfied with the prototype.
Developing an IT Help Desk Troubleshooter Expert System for diagnosing and solving IT Problems
2015
IT help Desk has been incorporated in many organizations to provide technical support for their employees and/or customers. In this paper, an IT help desk troubleshooter expert system has been developed based on the knowledge that has been captured from the network engineer (i.e. the expert) from Al Khawarizmi International College-UAE. Three main problems have been taken in consideration in building the system: printers' problems, Hardware/Software problems and Internet connection problems. The expert system has been developed using CLIPS. An interactive user interface has been added to the system to facilitate the interaction between the user and the system using Java. CommonKADS methodology has been used in this work to effectively design and analyze the system. The developed system achieves 81.8% accuracy as comparing to the human expert. The developed expert system can be used by any technician who may not have adequate knowledge about the domain problems.