Classifications as Linked Open Data. Challenges and Opportunities
Digital Libraries
2022-05-02 v1
Abstract
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.
Cite
@article{arxiv.2204.14066,
title = {Classifications as Linked Open Data. Challenges and Opportunities},
author = {Rick Szostak and Richard P. Smiraglia and Andrea Scharnhorst and Aida Slavic and Daniel Martínez-Ávila and Tobias Renwick},
journal= {arXiv preprint arXiv:2204.14066},
year = {2022}
}