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Research on the construction of traditional information science methodologytaxonomy is mostly conducted manually. From the limited corpus, researchershave attempted to summarize some of the research methodology entities intoseveral abstract levels (generally three levels); however, they have beenunable to provide a more granular hierarchy. Moreover, updating the methodologytaxonomy is traditionally a slow process. In this study, we collected full-textacademic papers related to information science. First, we constructed a basicmethodology taxonomy with three levels by manual annotation. Then, the wordvectors of the research methodology entities were trained using the full-textdata. Accordingly, the research methodology entities were clustered and thebasic methodology taxonomy was expanded using the clustering results to obtaina methodology taxonomy with more levels. This study provides new concepts forconstructing a methodology taxonomy of information science. The proposedmethodology taxonomy is semi-automated; it is more detailed than conventionalschemes and the speed of taxonomy renewal has been enhanced.

Using Full-text Content of Academic Articles to Build a Methodology Taxonomy of Information Science in China