Abstract
Ontolopedia, a Japanese-language ontology dictionary system intended for general use, was designed and constructed using the text of the Japanese-language version of the online encyclopedia Wikipedia as the corpus. In the present study, we evaluated the application of a knowledge map created from this dictionary system for accurately extracting and predicting the interests of individual users. The interests of each user were extracted and predicted from comments acquired via Twitter using the knowledge map constructed from Ontolopedia. Only nouns were extracted from the acquired data. Using the proposed technique and prepared vocabulary lists, words of interest were extracted and predicted for the following three items. First, each word in the vocabulary list was assigned a numerical score based on level of interest, and higher rank for words that generate more interest by users was verified. In order to verify these points, we compared our method with the widely used TF-IDF method. Second, we verified whether extraction of interest words was possible or not. The point of this discussion was carried out a situation in which the interest words don't appear directly in the vocabulary list. Third, we considered whether user interest characteristics could be determined by investigating interest biases with classified concepts on the knowledge map. As a result, the three kinds of evaluations showed that the proposed method aorded greater accuracy in extracting words of interest compared to conventional methods. Furthermore, we showed that the user interest biases could be observed. This observation result will improve the accuracy in extracting words of interest.
