You are here

Analysing and Improving Embedded Markup of Learning Resources on the Web

Authors: 

Stefan Dietze, Davide Taibi, Ran Yu, Phil Barker, Mathieu d'Aquin

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Web-scale reuse and interoperability of learning resources have been major concerns for the technology-enhanced learning community. While work in this area traditionally focused on learning resource metadata, provided through learning resource repositories, the recent emergence of structured entity markup on the Web through standards such as RDFa and Microdata and initiatives such as schema.org, has provided new forms of entity-centric knowledge, which is so far under-investigated and hardly exploited. The Learning Resource Metadata Initiative (LRMI) provides a vocabulary for annotating learning resources through schema.org terms. Although recent studies have shown markup adoption by approximately 30% of all Web pages, understanding of the scope, distribution and quality of learning resources markup is limited. We provide the first public corpus of LRMI extracted from a representative Web crawl together with an analysis of LRMI adoption on the Web, with the goal to inform data consumers as well as future vocabulary refinements through a thorough understanding of the use as well as misuse of LRMI vocabulary terms. While errors and schema misuse are frequent, we also discuss a set of simple heuristics which significantly improve the accuracy of markup, a prerequisite for reusing learning resource metadata sourced from markup.
Conference Name: 
26th International Conference on World Wide Web
Proceedings: 
WWW '17 Companion Proceedings of the 26th International Conference on World Wide Web Companion
Digital Object Identifer (DOI): 
10.1145/3041021.3054160
Publication Date: 
03/04/2017
Conference Location: 
Australia
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
No
Publication document: