that is used by searchers of varying backgrounds a more intelligent and proactive search aid is needed. The problems of information overload and vocabulary differences have become more pressing with the emergence of increas-The problems of information overload and vocabulary ingly popular Internet services. The main information retrieval differences have become more pressing with the emergence mechanisms provided by the prevailing Internet WWW soft-of increasingly popular Internet services [47, 24]. Although ware are based on either keyword search (e.g., the Lycos server Internet protocols such as WWW/http support significantly at CMU, the Yahoo server at Stanford) or hypertext browsing easier importation and fetching of online information (e.g., Mosaic and Netscape). This research aims to provide an sources, their use is accompanied by the problem of users alternative concept-based categorization and search capability not being able to explore and find what they want in an for WWW servers based on selected machine learning algoenormous information space [2,6,55]. While the Internet rithms. Our proposed approach, which is grounded on autoservices are popular and appealing to many online users, matic textual analysis of Internet documents (homepages), atdifficulties with search on Internet, we believe, will worsen tempts to address the Internet search problem by first categorizing the content of Internet documents. We report re-as the amount of online information increases. We consider sults of our recent testing of a multilayered neural network that devising a scalable approach to Internet search is criticlustering algorithm employing the Kohonen self-organizing cal to the success of Internet services and other current feature map to categorize (classify) Internet homepages ac-and future national information infrastructure applicacording to their content. The category hierarchies created could tions.serve to partition the vast Internet services into subject-specific The main information retrieval mechanisms provided by categories and databases and improve Internet keyword search-the prevailing Internet WWW-based software are based ing and/or browsing. © 1996 Academic Press, Inc.on either keyword search (e.g., the Lycos server at CMU and the Yahoo server at Stanford) or hypertext browsing (e.g., NCSA Mosaic and Netscape browser). Keyword INTRODUCTIONsearch often results in relatively low precision and/or poor recall, as well as slow response time due to the limitations Despite the usefulness of database technologies, users of the indexing and communication methods (bandwidth), of online information systems are often overwhelmed by controlled language based interfaces (the vocabulary probthe amount of current information, the subject and system lem), and the inability of searchers themselves to fully knowledge required to access this information, and the articulate their needs. Furthermore, browsing allows users constant influx of new information [11]. The result is to explore only a very small portion of the larg...

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This article describes research in the application of a Kohonen Self‐Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. Electronic brainstorming is one of the most productive tools in the Electronic Meeting System called GroupSystems. A major step in group problem solving involves the classification of electronic brainstorming output into a manageable list of concepts, topics, or issues that can be further evaluated by the group. This step is problematic due to information overload and the cognitive demand of processing a large quantity of textual data. This research builds upon previous work in automating the meeting classification process using a Hopfield neural network. Evaluation of the Kohonen output comparing it with Hopfield and human expert output using the same set of data found that the Kohonen SOM performed as well as a human expert in representing term association in the meeting output and outperformed the Hopfield neural network algorithm. In addition, recall of consensus meeting concepts and topics using the Kohonen algorithm was equivalent to that of the human expert. However, precision of the Kohonen results was poor. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information organization of textual information. Increasing uses of electronic mail, computer‐based bulletin board systems, and world‐wide web services present unique challenges and opportunities for a system‐aided classification approach. This research has shown that the Kohonen SOM may be used to automatically create “a picture that can represent a thousand (or more) words.” © 1997 John Wiley & Sons, Inc.

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