Experience from 10 months of University Linked Data

Mathieu d’Aquin - @mdaquin

Knowledge Media Institute, the Open University

LUCERO project

lucero-project.info – data.open.ac.uk

Linked Data

• As set of principles and technologies for a Web of Data– Putting the “raw” data online in a

standard, web enabled representation (RDF)

– Make the data Web addressable (URIs)

– Link with other data

Graph (up to date)

The Open University• The biggest university in the UK (200,000

students)• One of the youngest (40 years)• Most teaching done at a distance• 1 campus, 13 regional centers• Committed to “Open”:

– Open educational material available as podcasts (iTunes U), units of course material (OpenLearn), etc.

• Tradition of investing in new technology for teaching, learning, knowledge sharing, etc.– Role of the Knowledge Media Institute (KMi)

So Linked Data for the OU?

ORO

Archive of Course Material

Library’sCatalogueOf Digital Content

OpenLearnContent

A/V MaterialPodcastsiTunesU

Data from Research Outputs

BBC

DBPedia

DBLP

RAE

geonames

data.gov.uk

Currently: OU public data sit in different systems – hard to discover, obtain, integrate by users.

Exposed as linked data, our data interlink with each other and the external world: become part of the “global data space” on the Web

Why is it important?• The OU has been the first University to expose its data

as linked data: http://data.open.ac.uk• Now widely recognized as a critical step forward for the

HE sector in the UK (and worldwide)– Favor transparency and reuse of data, both externally and

internally– Reduces cost of dealing with our own public data: integration

and reuse by design– Enable both new kinds of applications, and to make the

ones that are already feasible more cost effective

• At least 3 other UK universities have now followed our example: – http://data.online.lincoln.ac.uk/, http://data.ox.ac.uk/,

http://data.southampton.ac.uk/– And others in other countries are setting up similar initiatives

The data.open.ac.uk Stack

Technical infrastructure

Organizational infrastructure

Institutional repository data

Research Data (Arts)

Applications

data.open.ac.uk

Planning + Logging

Collect Extract Link Store Expose

OntologiesScheduler

RSS Updater Triple Store

Delete (1)Add (2)

Index Search

SPARQLendpoint

Web Server

RSS Extractor

XML Updater

RDF Extractor

RDF Cleaner

Cleaning rules

Each datasets

Lib, courses, loc

ORO, podcast

URL redirection rules

RSS feed

New itemsObsolete items

RDF file (add) RDF file (delete)

RDF file (add) RDF file (delete)

Generic process Dataset specific process

Entity Name

SystemURI creation rules

Method for a exposing a dataset

Initial Meeting with Data Owner

- Identify data- Get sample data- Identify Copyright Issues- Identify possible links- Identify users and usage

Data Modeling sessions

Lucero Core Team

Data Owner

Lucero KMi Team

Lucero members

- Find reusable ontologies- Map onto the data- Identify uncovered parts- Define URI Scheme

Data Modeling Validation

Lucero Core Team

Data Owner

Development of Extractor

URI Creation Rules

DefinitionDeploymentLucero KMi

Team

Screenshot of the dataset page

Applications• For education

– Mobile podcast explorer, podcast explorer on TV – OU Building Map, OU location tracker (cf.

foursquare)– OU Expert Search– Connecting courses/OpenLearn to relevant

podcast– OU Course Profile Facebook app using list of

courses, “Study Buddy” app connecting facebook users to relevant courses

• For Research– Display connections in a research community– Research Data/Impact Analysis– Connection research datasets to external data

Example application: Link OpenLearn to relevant course/podcasts

Example Application: keep track of location, meetings, tutorials, at the OU

Example application: exploring research communities

EXAMPLE APPLICATION:Expert Search using publication information and connecting to contact information within the OU

Example application: Explore Information about a person in the “Reading Experience Database” based on data provided by DBPedia (Linked Data version of Wikipedia) New ways to look at humanities research data

Lessons Learnt• The major part of the work is not technical

– Linked data is simple!– Identifying available data, obtaining access to them, re-

modeling them is hard– Making people understand that it is worth doing is critical– Especially when dealing with challenges such as data

licenses, private data, etc.

• Get people involved (it is not about you, or the technology)– A lot of people’s job (administrators, managers, researchers) is

all about collecting and managing data– A lot of this effort is lost because of closed systems, lack of

integration and exposure of the data– Our job is to demonstrate to these people how the principles of

linked data can be used to leverage this effort – Without being disruptive (e.g., the URI of a course in a browser

redirects to the course webpage on the OU website

Lessons Learnt• There is no killer app

– The direct benefit of linked data is not in a great big smart application, it is in the many small things that are made easier

– Need to make it easy for developers to get into it, play with it, see the potential by themselves

– Integrating the benefits of linked data in the university’s practices/workflows takes time. It is not a threatening big change, but a slow, incremental adoption

• Plan for long term = need for endorsement– We work with the assumption that, soon, it will be as common and necessary

for a University to have a linked data platform as it is to have a website– So a linked data initiative at a university cannot be a one time thing. Courses

evolve, new material appear, new datasets are made available. (e.g., data.open.ac.uk is updated every day)

– It needs to become part of the University’s role and be endorsed by the departments involved (IT, communication, education, research, business)

• It does not always work– Some applications might be incompatible with the University’s policies (e.g.,

Google rich snippet showing the price of a course)– Support might only get up to a certain point

The future • From nice demonstrators to real semantic web

applications– Use of reasoning and data mining for data consolidation and

analysis– Need proper frameworks for application developers!

• Linked data and the Semantic Web to support research– Not only research communities– Identifying new research questions and collecting evidence

through connected datasets

• It is not about individual Universities!– Universities sharing data to benefit students and researchers:

the higher education’s web of linked data– Needs collective vocabularies, recipes, approaches,

classifications… the GoodRelations of higher education?

The future• Linked data

analytics/Linked data mining

• Interfaces to linked data/Making sense of linked data (with ontologies)

• Semantic web for activity data/personal data

Thank you!

Carlo Allocca (Dev)

Mathieu d’Aquin(PD)

Salman Elahi((Ex)-Dev)

Enrico Motta(SGP)

Andriy Nikolov(linking)

Jane Whild(Admin)

Fouad Zablith(Dev)

Library Specialists

Owen Stephens(PM)

Richard Nurse((ex-)PM)

Non ScantleburyArts Specialists

Suzanne Duncanson-HunterJohn Wolfe

Paul LawrenceStuart Brown

Data Owners

KMi

OU Library

Com./StudentComp.Services

Arts