Automatic extraction of historical transition in researchers and research topics

It is necessary for a researcher to know historical transition in researchers and research topics. Although Web search engines can be used for obtaining such information, collecting the information across a long time period is difficult and laborious. Thus, we proposed a method for automatically extracting historical transition in researchers and research topics by using co-occurrence information. We used an original method in which a concept that co-occurred more often with a certain concept X near the time when the concept X was generated was more likely to be the root of the concept X. We compared our method with the previous method proposed by Kawanaka et al., where transition information on concepts was automatically extracted by analyzing tags that describe concepts in social bookmarks, and we confirmed that our method outperformed their method. The accuracies of the extracted transition information in researchers and research topics in our method were 0.66 and 0.65 respectively, whereas the corresponding accuracies in Kawanaka et al.´s method were 0.25 and 0.61.