Text mining and knowledge discernment: An exploratory investigation

Data Mining and Knowledge Discovery have been proven as a means of uncovering hidden information in alphanumeric databases. These databases are based on fields in relational or flat file structures. There is a correlation between the fields and the information contained in them. It has been estimated that over 80% of all written material first occurs in electronic form. The emergence of initial efforts in Text Mining appears to be promising a method of uncovering hidden information in textbases (electronic textual repositories). The textbases are a collection of free-form, connected discourse. The implications are that the successful implementation of such a methodology for uncovering hidden information would provide researchers with unsuspected insights into previously published material.; The purpose of this work was to investigate the methodology that defines the steps required for text-mining and knowledge-discernment in textbases and evaluate the existing instruments. This work assessed the state of the field. It provided a broader evaluation of the requirements of text-mining in light of the functionality provided by data-mining to databases. Furthermore, this work investigated the published data on text-mining tools, developed the functionality required for text mining, and evaluated the application of the text-mining tool(s). Testing the functionality of the tool(s) with both a descriptive and a results oriented textbase provided an evaluation of the state of the knowledge discernment technology for textbases. Building on the findings, a recommendation was made for the developmental needs for future tools. The output of this work is a process description of the elements required for text-mining and knowledge-discernment to be applicable to technical information. The results of this work provide guidance for future developmental efforts in text mining.