Science and Technology Text Mining Basic Concepts

Abstract

This literature review presents a broad array of techniques that are becoming available to mine textual data. The review initially presents a three-function (i.e., data collection, data warehousing, and data exploitation) text mining architecture consisting of a six-step text mining process (i.e., source selection, text retrieval, information extraction, data storage, data mining, and presentation). It then presents some of the most widely used data and text mining techniques, including clustering and classification methods, such as nearest neighbor, relational learning models, and genetic algorithms, and dependency models, including graph-theoretic link analysis, linear regression, decision trees, nonlinear regression, and neural networks. The review illustrates some of their potential by describing the Office of Naval Research (ONR) text mining pilot program. In the first year of that program, existing metadata from commercial bibliographic databases were used. There is presently an unacceptably long delay between the development of key component technologies for textual data mining and the deployment of the integrated tools that S&T sponsors need. The first year of the ONR text mining pilot program represents an initial attempt to bridge that gap. Important lessons have been learned about the use of text mining for the management of science and technology research, but much remains to be done. (37 refs.)

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