Over the years, information science professionals have been studying biases in Knowledge Organization Systems (KOS), for example, bibliographic classifications. The robustness of classifications has been examined in diverse measures, ranging from the representation of race, gender, ethnic minorities, to indigenous peoples. In this study, we aim at (a) uncovering implicit assumptions about minorities in everyday taxonomies; (b) comparing and reconciling these different taxonomies. Specifically, we study the use case of Taiwanese Indigenous Peoples' tribe classifications and the indigenous constituencies of the legislature electoral representation. We compare four finer-grained taxonomies for indigenous people with the coarse-grained indigenous peoples' electoral constituencies that only recognize two regions (Lowland, Highland). The four taxonomies are: the recognized tribes in the past, the recognized tribes in the present, other possible tribes, and re-scaled groups based on population. We employ a logic-based taxonomy alignment approach using Region Connection Calculus (RCC-5) relations to align these taxonomies. Our results show different options when modeling and interpreting the use case of Indigenous Taiwan constituencies, and also demonstrate that multiple perspectives can be preserved and co-exist in our merged taxonomic representations.
|Proceedings of the Association for Information Science and Technology
|Published - 2020
- knowledge organization
- taxonomy alignment
ASJC Scopus subject areas
- General Computer Science
- Library and Information Sciences