Presenting and preserving the change in taxonomic knowledge for linked data

Taxonomic knowledge provides a scientific name to each organismal group and is thus indispensable information for understanding biodiversity. However, the various perspectives of classifying organisms and changes in taxonomic knowledge have led to inconsistent classification information among different databases and repositories. To have a precise understanding of taxonomy, one needs to integrate relevant data across taxonomic databases. This is difficult to establish due to the ambiguity in taxon interpretation. Most researchers in earlier stages employed the Linked Open Data (LOD) technique to establish links in taxonomy transition. However, they overlooked the temporal representation of taxa and underlying knowledge of the change in taxonomy, so it is difficult for learners to gain perspective on how some identifiers of taxa are linked. To this end, this research is aimed at developing a model for presenting and preserving the change in taxonomic knowledge in the Resource Description Framework (RDF). Specifically, the proposed model takes advantage of linking Internet resources representing taxa, presenting historical information of taxa, and preserving the background knowledge of the change in taxonomic knowledge in order to enable a better understanding of organisms. We implement a prototype to demonstrate the feasibility and the performance of our approach. The results show that the proposed model is able to handle various practical cases of changes in taxonomic works and provides open and accurate access to linked data for biodiversity.