论文标题
分类为链接的开放数据。挑战和机遇
Classifications as Linked Open Data. Challenges and Opportunities
论文作者
论文摘要
链接的数据（LD）作为基于网络的技术可以原则上实现了无缝的，机器支持的集成，各种知识的相互作用和增强，并将其标记为巨大的知识图。尽管网络技术数十年以及最近的LD方法，但在公共领域中充分利用这些新技术的任务只是开始。一个具体的挑战是传输preweb开发的技术，以将我们的知识命令进入链接的开放数据领域（LOD）本文说明了两个不同的模型，其中一般的分析 - 合成分类可以发表并作为LD提供。在这两种情况下，LD解决方案都涉及预先协调的索引语言的复杂性。
Linked Data (LD) as a web--based technology enables in principle the seamless, machine--supported integration, interplay and augmentation of all kinds of knowledge, into what has been labeled a huge knowledge graph. Despite decades of web technology and, more recently, the LD approach, the task to fully exploit these new technologies in the public domain is only commencing. One specific challenge is to transfer techniques developed preweb to order our knowledge into the realm of Linked Open Data (LOD) This paper illustrates two different models in which a general analytico--synthetic classification can be published and made available as LD. In both cases, an LD solution deals with the intricacies of a pre--coordinated indexing language.
