A Text Mining Approach to Uncover the Structure of Subject Metadata in the Biodiversity Heritage Library
Yi‐Yun Cheng,
Nikolaus Nova Parulian,
Ly Dinh
Abstract:We propose a bottom‐up, data‐driven pipeline to uncover the structure of biodiversity subject metadata using a combination of text mining approaches. In this study, we analyze 721,035 subject terms in the Biodiversity Heritage Library (BHL). We utilize named entity recognition and word‐embedding methods to systematically label and group terms based on their vector‐space distances. The results show that the subject terms from BHL are clustered into several prominent themes relating to environmental regulations,… Show more
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