Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Name : 35th Fuzzy System Symposium
Number : 35
Location : [in Japanese]
Date : August 29, 2019 - August 31, 2019
One of the methods of utilizing patent publications, which are intellectual property information, is to classify them from their own viewpoint and grasp trends. However, people meet the following issues. (1) The results diversify because each person's perspective on grasping the contents is different.(2) It is difficult to share the accumulated information with others because the results of classification and the diversification of classifications.(3) It takes a lot of man-hours to understand the contents. To support this task, I am developing a Rough Set Theory based categorization system for patents with machine translation. I explain the algorithm of this system and the evaluation result about it in this paper. The proposed system makes decision rules based on Rough Set Theory from categorized patents. Then it categorizes unlabeled patents by matching rules with them. Those rules are based on terms, so the flexible expression is big problem. To solve it, I make decision rules from translated patents by machine translation system. I implemented a prototype system based on this approach and categorizing patent publications in Japanese with experts. My approach shows the accuracy as about 0.5. The performance of my system is almost the same as the performance with Naive Bayes Classifier. I say my approach has a possibility to reduce the load of categorization patents, but the performance is not enough. To improve my approach, I will analyze the experimental results, propose new algorithm and evaluate using actual patents.
