Building a Linked Data Graph for Education
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1 Building a Linked Data Graph for Education Dr Tom Heath tom.heath@talis.com Talis Education Ltd SemTechBiz, London, September 2012
2 What do we mean by an 'Education Graph'? The set of all (connected) entities that have an active or potential bearing on the education process (initially focused on higher education)
3 Why do we care about an 'Education Graph'? Because learning is artificially disconnected An education graph can bridge those disconnects
4 Components of the Education Graph Course Descriptions Publishers Khan Academy, P2PU, etc Scheduling Course Content Biblio Databases Students and Teachers Subject Codes Wikipedia Facebook etc Youtube
5 Components of the Education Graph Course Descriptions Publishers Khan Academy, P2PU, etc Scheduling Course Content Biblio Databases Students and Teachers Subject Codes Wikipedia Facebook etc Youtube
6 Talis Aspire Reading Lists
7 Talis Aspire Reading Lists >40 enterprise customers in the UK and beyond = 1/3 UK universities 10,000s of reading lists 100,000s of learning resources Heavy usage with interesting peaks in demand
8 Talis Aspire Reading Lists An RDF-native application, hosted by us Backed by a hosted triplestore Migrating to MongoDB Linked Data views available on the public Web A real, live Linked Data application with paying customers (Probably) the most heavily used Linked Data application in the education domain
9 Components of the Education Graph Course Descriptions Publishers Khan Academy, P2PU, etc Scheduling Course Content Biblio Databases Students and Teachers Subject Codes Wikipedia Facebook etc Youtube
10 Building and Enriching the Education Graph
11 Problem From Plain Text to a 'Biblio-graph-ic' Record 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...
12 Enhancing Data Quality with Entity Reconciliation Validate the accuracy of the record by matching against high-quality reference data sources OpenLibrary, OpenKB (serials/journals), CrossRef Books: match on a precise edition, roll up to work, map to our canonical resource Articles: enrich the graph describing the resource using OpenKB, search CrossRef using enriched description, map to our canonical resource
13 A Happy By-Product Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.
14 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 Reading List store Repeat the entity reconciliation process Retain the links mapping canonical resources to institutional data
15 Warehousing the Education Graph Goal a single point of access to the education graph Applications analytics and business intelligence (for us and customers) data science experiments Approach based on Apache Jena TDB/Fuseki + MongoDB
16 Applications of the Education Graph
17 Directory of Quality Learning Resources
18
19 Recommending Learning Resources
20 Reflections
21 Reflections What we think matters may not be important e.g. native RDF apps vs. Linked Data views online
22 Reflections What we think matters may not be important e.g. native RDF apps vs. Linked Data views online Tooling Reality Check how do we measure up compared to e.g. NoSQL databases, Hadoop, etc?
23 Reflections What we think matters may not be important e.g. native RDF apps vs. Linked Data views online Tooling Reality Check how do we measure up compared to e.g. NoSQL databases, Hadoop, etc? Is numerical data a second-class citizen of the graph?
24 Questions? Web: talisaspire.com YouTube: youtube.com/user/talisaspire Facebook: facebook.com/talisaspire
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