  Research on the construction of traditional information science methodology
taxonomy is mostly conducted manually. From the limited corpus, researchers
have attempted to summarize some of the research methodology entities into
several abstract levels (generally three levels); however, they have been
unable to provide a more granular hierarchy. Moreover, updating the methodology
taxonomy is traditionally a slow process. In this study, we collected full-text
academic papers related to information science. First, we constructed a basic
methodology taxonomy with three levels by manual annotation. Then, the word
vectors of the research methodology entities were trained using the full-text
data. Accordingly, the research methodology entities were clustered and the
basic methodology taxonomy was expanded using the clustering results to obtain
a methodology taxonomy with more levels. This study provides new concepts for
constructing a methodology taxonomy of information science. The proposed
methodology taxonomy is semi-automated; it is more detailed than conventional
schemes and the speed of taxonomy renewal has been enhanced.
