OpenLearn resources add further semantic flexibility to their resources

Tuesday 13:00-14:30 (12), Mezzanine Balcony

Type: Poster

Theme: Building and linking communities of open practice

#oer14 #abs123

Authors

Ms Lara Whitelaw, University Metadata Development Manager, The Open University, [email protected]

Abstract

Introduction

OpenLearn offers free educational resources and courses from The Open University for all. The Open University was approached by the Learning resource Metadata Initiative (LRMI) last year and asked to consider encoding OpenLearn resources using LRMI. LRMI is a schema.org initiative that ‘aims to make it easier to publish, discover, and deliver quality educational resources on the web’ (LRMI, n.d.). The poster demonstrates how we set out to develop OpenLearn LRMI metadata in RDFa Lite, following the principles of the Semantic web.

Methods

Schema is a collection of metadata tags that can be added to web pages to allow search engines like Google, Bing and Yahoo to bring back enhanced search results. ‘Rich snippets’ is one of the ways search results can be enhanced , giving users quicker access to information and making it easier for them to decide which search results are relevant to them. The use of rich snippets is widely reported to significantly increase user selection of a search result. The poster will contain an example of a rich snippet as it would be displayed in a google search result

The Open University is committed to developing our learning materials for the Semantic Web where ever possible; we have a growing collection of Linked Data on data.open.ac.uk. Linked Data is a Semantic Web term used to describe a method of exposing and connect web-based resources by structuring pages in a way that enables computers to identify and create connections between them.

When we were approached by LRMI, their example datasets were all in microdata (W3C, 2013), which although machine-readable, is not compatible with RDF; the underlying mark-up language Linked Data is written in. Due to the limitations of microdata and the OU’s commitment to RDF, we made the decision to encode our resources in RDFa Lite. RDFa Lite (W3C, 2012), is an approved W3C standard and if fully compatible with RDFa and semantic technologies.

Results

We have now successfully implemented and launched our RDFa Lite based LRMI metadata on OpenLearn, the Google Rich snippets testing tool allows you to see how the data can be utilised by the search engines to bring back more useful and informative search results (GoogleWebmaster Tools, n.d.).

We believe that encoding our resources in RDFa Lite makes our resource much more flexible, allowing us to make our resources available to Google for enhanced search results and search facets, and further extend the capabilities of the semantic web tools that we are developing to enable integration of OpenLearn materials and BBC content, such as http://discou.info/. It will also allow us to augment and enrich the information we can provide to our users, as we will be able to utilise semantic technologies to share, reuse and combined our resources with other Linked Data repositories and web sites.

We intend to do further work incorporating LRMI onto our OpenLearn assets, with a key objective of the coming year being to map our data to subject and educational level frameworks.

References

LRMI, n.d. LRMI Learning Resource Metadata Initiative, About the LRMI [online]. LRMI. Available from: http://www.lrmi.net/about (Accessed 24 January 2014).
W3C, 2013. HTML microdata, W3C Working Group note 29 October 2013 [online]. W3C. Available from: http://www.w3.org/TR/microdata/ (Accessed 24 January 2014).
W3C, 2012. RDFa lite 1.1, W3C Recommendation 07 June 2012 [online]. W3C. Available from: http://www.w3.org/TR/rdfa-lite/ (Accessed 24 January 2014).
Google Webmaster Tools, n.d. Structured Data Testing Tool[online]. Google. Available from: http://tinyurl.com/og59zsg (Accessed 24 January 2014).

Files

• OpenLearn and LRMI poster-final.pdf

Recap recording

A presentation of this poster is available to view at https://vimeo.com/93892788.

Further details

Keywords: metadata, semantic web, RDFa, LRMI