Classifications as Linked Open Data. Challenges and Opportunities

TL;DR

This paper explores how to publish complex classification systems as Linked Open Data, addressing challenges in integrating pre-coordinated indexing languages into the LOD framework.

Contribution

It presents two models demonstrating the publication of analytical-synthetic classifications as Linked Data, highlighting methods to handle pre-coordinated indexing languages.

Findings

Two models for publishing classifications as Linked Data

Effective handling of pre-coordinated indexing languages in LOD

Facilitates integration of complex classifications into the semantic web

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.