Presenting and preserving the change in taxonomic knowledge for linked data
Presenting and preserving the change in taxonomic knowledge for linked data
复制标题
呈现和保存链接数据的分类知识的变化
作者：
Rathachai Chawuthai;Hideaki Takeda;V. Wuwongse;U. Jinbo
分类学知识为每个生物群提供了一个科学名称，因此是理解生物多样性不可或缺的信息。然而，生物分类的不同视角和分类学知识的变化导致不同数据库和知识库之间的分类信息不一致。为了准确理解分类学，需要整合分类学数据库中的相关数据。这是很难建立，因为在分类单元解释的模糊性。早期的研究者大多采用开放数据链接（LOD）技术来建立分类学转换中的链接。然而，他们忽略了分类群的时间表示和分类学变化的基础知识，因此学习者很难获得分类群的一些标识符是如何联系在一起的。为此，本研究的目的是开发一个模型，用于呈现和保存资源描述框架（RDF）中的分类知识的变化。具体而言，该模型利用互联网资源代表分类群，提出分类群的历史信息，并保留在分类知识的变化，以使更好地了解生物的背景知识。我们实现了一个原型，以证明我们的方法的可行性和性能。结果表明，所提出的模型是能够处理各种实际情况下的分类工作的变化，并提供了开放和准确的访问链接数据的生物多样性。
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.