Patent text is a rich source to discover technological progresses, useful to understand the trend and forecast upcoming advances. For the importance in mind, several researchers have attempted textual-data mining from patent documents. However, previous mining methods are limited in terms of readability, domainexpertise, and adaptability. In this paper, we first formulate the task of technological trend discovery and propose a method for discovering such a trend. We complement a probabilistic approach by adopting linguistic clues and propose an unsupervised procedure to discover technological trends. Based on the experiment, our method is promising not only in its accuracy, 77% in R-precision, but also in its functionality and novelty of discovering meaningful technological trends.

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A blog is a new media that is receiving a lot of attention. Its links enable us to get a hold of social relations between bloggers in a blog space, and the relations reflect bloggers' interests. Therefore, the ability to search documents in linked blogs is significant for bloggers. An egocentric search method was proposed to search for documents in such neighboring blogs. However, it takes quite considerable time to find the most valuable documents in a user's neighboring blogs when many blogs are linked to that user's blog. Therefore, the purpose of our study is to improve the egocentric search speed for important documents in the neighboring blogs. To achieve this goal, we are proposing a rapid egocentric search scheme that reduces the search space to more important blogs. Our study shows that the number of neighboring blogs, which are linked to a blog with trackbacks and comments, is important for estimating the authority of blog. In the experimental results, our method was four times as fast as the egocentric search using a breadth-first search strategy in searching for the Contents Abstract ....

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