Fisheye Matching: Viewpoint-sensitive feature generation based on concept structure

Recent rapid growth of information environment such as the Internet makes it easy for us to get vast information. On the other hand, `information overflow' is becoming a serious problem. To cope with such a problem, we have extended the normal Vector Space Model (VSM) to reflect the users' viewpoints more clearly. We call this new matching method the Fisheye Matching method, which generates the features related to the users' viewpoints based on the concept structure of an electronic dictionary. In the Fisheye Matching method, the users' viewpoints are expressed as a set of word groups, each of which corresponds to a certain concept in the concept structure. Each concept in the dictionary has heading information, and the users can grasp their viewpoints easily from such information. Experimental results on information retrieval show that the Fisheye Matching method can not only retrieve documents in which the users take interest, but also supply them with useful information on their viewpoints.