Rule Based Inference Engine For Classification of Expert Systems Using Keyword Extraction Technique

Abstract

Because of the fast-growing demands in automated document dispensation, a steadfast system for automatic identification of keywords entrenched in an electronic document is of immense concern. The research envisaged an innovative approach for the classification of multiple Expert System (ES) methodologies at a time on the basis of keyword extraction using a commercial text mining tools WordStat and Compare- Suite Pro. These ES methodologies include eleven categories that include; rule-based systems, database methodology ,case-based reasoning, intelligent agent, knowledgebased systems, fuzzy based expert system, object oriented methodology, neural networks, system architecture, systems, modelling, and ontology. The keywords are selected on the basis of frequency analysis and position of most recurring word in context within the article tile, abstract and keywords of respective ES methodology. Based on the extracted keywords, an inference engine has been designed on java software. This software compares the keyword established from the articles of individual ES methodology with all other articles of the remaining methodologies using association of general rule-based system. The inference engine developed was first calibrated for 100 articles out of 160 and then validated for remaining 60 articles. The validation results shows accuracy of the experimental results more than 85 percent. The study concludes that the classification of Expert Systems using keyword extraction technique, outperforming a base line, is a more accurate, reliable and optimal with respect to time as compared to other orthodox methods of text mining. At the end it has been concluded that the techniques may further improved by limiting design constraints in the tools adopted in the research for future endeavours.