Towards the Ethnic Understanding of Taiwanese Indigenous Peoples: A Mashup Based on Semantic Web and Open Data

Abstract

The purpose of this study is utilizing semantic web technologies to mix diverse datasets of Taiwanese indigenous peoples to facilitate ethnic understanding. Because indigenous peoples have been in a lower social grade for centuries, indigenous culture and corpus are relatively rare in the modern society. Insufficient information not only enlarge the gap of misunderstanding but lead to the emotional alienation between ethnics. To integrate multiple data sources, a data mashup is a way to achieve the integration of data. Furthermore, the result of mashups may offer new insights to discover interesting knowledge. To implement data mashup on the Web, three components are identified: (1) collecting available open data related to indigenous people; (2) performing a data conversion process to convert non-RDF to RDF data; (3) developing SPARQL federated queries to mix data from diverse endpoints. Furthermore, a web-based application based on an interactive map is utilized to facilitate ordinary users to find information from the general information of tribes to cultural artworks. The experimental results show that data mashup design can help users quickly understand essential information of indigenous peoples.