Slide deck accompanying the paper of the same name, presented at the Linked Learning 2012 workshop at WWW2012, Lyon, France. The abstract of the paper reads: This paper introduces the notion of the education graph, a conceptual representation of the resources and interconnections at the heart of the learning process. We present our latest work on the Talis Aspire family of products that,
through the use of Linked Data principles and technologies, enables the assembly and application of a rich education graph based on learning resources used in tens of UK universities. Techniques for entity extraction and reconciliation across data sources are presented, in addition to descriptions of recommendation generation from portions of this education graph.
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Assembling and Applying an Education Graph based on Learning Resources in Universities
1. Assembling and Applying an
Education Graph based on Learning
Resources in Universities
Tom Heath, Ross Singer, Nadeem Shabir,
Chris Clarke and Justin Leavesley
Talis Education Ltd
LiLe2012, Lyon, 17th April 2012
2. What do we mean by an
'Education Graph'?
● The Web is a graph of documents
● Facebook, LinkedIn, etc. capture elements of a
'social graph'
● The Web of Data is one big, heterogeneous graph
encoded in RDF
● The 'education graph' is a portion of that graph
concerned with learning and teaching
3. Overview
● Talis Aspire and the institutional sub-graph
● Applications of a broader education graph
● Ongoing and Future Work
5. Talis Aspire Campus Edition
● ~30 customers in the UK and beyond
● 10,000s of reading lists
● 100,000s of learning resources
● Loads of users every day!
● Backed by a hosted triplestore
● Linked Data views available on the public Web
● A real, live Linked Data application that people pay for
● (Probably) the most heavily used Linked Data application
in the education domain
7. From Plain Text to a
'Biblio-graph-ic' Record
● Problem
● Only some data is entered in structured form
● Legacy data is typically plain text citations
● Our Approach
● Pre-process citation text with regex
● Pass through heavily modified version of FreeCite
● Clean output again with regex
● Return as JSON object
● Pass through entity reconciliation process...
8. Enhancing Data Quality with
Entity Reconciliation
● Validate the accuracy of the record by matching
against high-quality reference data sources
● Data sources
● OpenLibrary, OpenKB (serials/journals), CrossRef
● Process
● Books: match on a precise edition
● Articles: enrich the graph describing the resource using
OpenKB, search CrossRef using enriched description
● Map record to canonical resource
9. A Happy By-Product
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
10. Unifying the Institutional Sub-Graphs
● Goal
● Create a cross-institution (portion of) the education
graph, centred around learning resources
● Process
● Harvest the data from each Campus Edition triplestore
● Repeat the entity reconciliation process
– Retain the mapping of canonical resources to those on
institutional lists
14. Ongoing and Future Work
● Evaluation of recommendation quality
● Role/importance of list length, list position, list sections,
section ordering
● Linked Data-based data warehousing infrastructure
(for analytics and prototyping)
● Alternative approaches to triple-storage
● Integration of other portions of the education graph
