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.
