SOMSE: A semantic map based meta-search engine for the purpose of web information customization

To combat information overload, systems that are often referred to as information customization systems are needed. Such systems act on the user's behalf and can rely on existing information services like search engines that do the resource-intensive part of the work. These systems will be sufficiently lightweight to run on an average PC and serve as personal assistants. Since such an assistant has relatively modest resource requirements it can reside on an individual user's machine. If the assistant resides on the user's machine, there is no need to turn down intelligence. The system can have substantial local intelligence. In this paper, we propose an information customization system that combines meta-search and unsupervised learning. A meta-search engine simultaneously searches multiple search engines and returns a single list of results. The results retrieved by this engine can be highly relevant, since it is usually grabbing the first items from the relevancy-ranked list of hits returned by the individual search engines. The Kohonen Feature Map is then used to construct a self-organizing semantic map such that documents of similar contents are placed close to one another.