Judging from the discussion afterwards and some reactions on twitter, this was very much welcomed by the audience, especially as an encouragement for members of other universities to get on board the linked data train. I also demoed our latest prototype: DiscOU. It is a “discovery engine” for open educational resources which, in the current demo, can find them from BBC programmes (including iPlayer). We are actually pretty excited about this as it not only applies linked data as well as semantic search techniques to make itself more meaningful and customisable than other recommender tools, but also because it opens the way for a lot of potentially great applications, such as finding open educational resources relevant to museum exhibitions or to courses in other universities.

Now, even more interesting from this presentation and the discussions that followed was the new projects, going beyond Linked Data at the Open University. Linked Universities for example is growing pretty strong, with more datasets, vocabularies and tools being added on a regular basis, and more people getting in touch to discuss the application of linked data in their institution. We also just kicked off a new project, an EU support action called LinkedUp, that is all about web data from across various origins used to create new, innovative educational services. Finally, I’m getting involved in the development of Marimba, a tool developed in Madrid originally for the Spanish National Library to extract linked data from MARC-based library catalogues using customised mappings. We are now working on providing this tool to other universities, including university libraries.

Anybody how would monitor the job vacancies page on the KMi website can see that there are very exciting news coming up: we are hiring a whole team to take care of different aspects of Linked Open Data, at the Open University and beyond. This is exciting because it means that we are given the opportunity to make linked data a core part of the university’s information infrastructure, and through this, to offer to three talented people the chance to shape the way the higher education sector share, reuse and connect open data for the benefits of both teachers and learners. It is exciting also because each of these jobs include both a strong innovative component (working with state-of-the-art technologies and developing applications never seen before) and a direct relationship with the community of users of these technologies, whether it is researchers at the OU, the entire university or even a whole network of universities worldwide.

I describe the jobs quickly below with the links to the complete job descriptions and information about the way to apply. Please contact me if you have any question, or if you just want to discuss the jobs (using email, twitter, comments, or whatever other means…)

Project Officer – Linked Data (1 year)

This is a grade 7 job (for those of you who know what that mean) which basically is about becoming the Linked Data Specialist/Expert/Champion at the Open University. The core of the work here is basically to take care of data.open.ac.uk: ensure that it works and is maintained, keep the data up-to-date with changes in the original sources, identify new sources of data and integrate them, demonstrate and build the practices of Linked Data within the University, especially through dedicated applications. We are therefore expecting candidates to have good technological background (with the ability to develop using languages such as Java, PHP, Javascript) and good communication skills. Naturally, an interest in linked (open) data is strongly recommended.

The deadline for applying is the 19th September 2012. See the vacancy page for details.

Project Officer – REF Publications Linked Data (1 year)

This is also a grade 7 job. One of the most significant areas of application of Linked Data at the Open University (and, I believe, the academic sector in general) is for the management of research communities and research outputs. This job concerns one of such applications, which is especially dedicated to supporting the Research Excellence Framework for evaluating the quality of research realised in the university. This application makes use of data available from data.open.ac.uk and creates its own (private) datasets to support researchers at the Open University (hundreds of them) in managing, annotating and promoting their research output. Ability to programme (in PHP, and possibly in Javascript) as well as experience/interest in managing information in the academic community are of course needed here.

The deadline for applying is the 19th September 2012. See the vacancy page for details.

Research Assistant / Associate — LinkedUp Project (2 years)

More academic in nature (AC1/2), this job is also broader in scope. Working within the LinkedUp European Support Action, the goal here is to create both the technological and the support infrastructure to push forward the application of linked data in the education sector in general. This means in particular that we need somebody able to become an expert in the application of the linked data principles for open educational data, and learning and teaching applications. Beyond the purely technological aspects however, this implies collaborating and engaging with a large network of educational institutions (universities and other organisations) on innovations led by the deployment of Linked Data-based solutions.

The deadline for applying is the 12th September 2012. See the vacancy page for details.

This report is based in large parts on the experience built in the last months of LUCERO, especially in connecting with other organisations and trying to gather common issues and practices, through LinkedUniversities.org.

The report covers the general principles underlying Linked Data technologies and their relevance to the field of education, especially focusing on open and distance learning. It illustrates use case scenarios of Linked Data for learning and teaching through describing existing applications, and details the process of adopting and deploying Linked Data for educational resources and learning-related information. Through publishing it on its website under an open license, COL hopes that this report will become a valuable resource to a wide variety of organisations, raising the general awareness of the benefits of using open Web technologies such as Linked Data for educational purposes.

The Datasets

To look at the current state of linked data in education, we considered 8 different datasets, some provided by universities and some by specific projects. We looked at datasets that were explicitly dedicated to education (as opposed to the ones containing information that could be used for educational purposes, such as library and museum data, and the ones that have connection with education but focus on other aspects, such as the datasets from purely research institutions). Also, we view datasets in a very coarse-grained way, for example considering the whole of data.open.ac.uk as one dataset, rather than each of its sub-datasets separately. Finally, we could only process datasets with a functioning SPARQL endpoint working properly with common SPARQL clients (in our case ARC2).

From Universities:

• data.open.ac.uk which SPARQL endpoint is available at http://data.open.ac.uk/sparql
• data.bris from the University of Bristol. SPARQL endpoint: http://resrev.ilrt.bris.ac.uk/data-server-workshop/sparql
• University of Southampton Open Data. SPARQL endpoint: http://sparql.data.southampton.ac.uk/
• LODUM from the University of Muenster, Germany. SPARQL endpoint: http://data.uni-muenster.de/sparql

Others should be included eventually, but we could not access them at the time

From projects and broader institutions

• mEducator, a european project aggregating learning resources: SPARQL Endpoint: http://meducator.open.ac.uk/resourcesrestapi/rest/meducator/sparql
• OrganicEduNet a european project that aggregated learning resources from LOM repositories (see this post). SPARQL endpoint: http://knowone.csc.kth.se/sparql/ariadne-big
• LinkedUniversities Video Dataset which aggregates video resources from various repositories (see this paper). SPARQL Endpoint: http://smartproducts1.kmi.open.ac.uk:8080/openrdf-sesame/repositories/linkeduniversities
• Data.gov.uk Education which aggregates information about schools in the UK. SPARQL endpoint: http://services.data.gov.uk/education/sparql

Common Vocabularies

As everybody will always say: the important thing is the reuse shared and common vocabularies! As they are talking about similar things, it is expected that education-related datasets would share vocabularies, and that their overlaps would allow to achieve joint reuse of the exposed data. The chart above shows the namespaces that are used by more than one of the considered datasets.

Unsurprisingly, FOAF is almost omnipresent. One of the reasons for this is that FOAF is the unquestioned common vocabulary to represent information about people, and it is quite rare that an education-related dataset would not need to represent information about people. It is also the case that FOAF includes high-level classes that are also very common, especially in this sort of datasets, namely Document and Organisation.

In clear second place come vocabularies to represent information about bibliographic resources, and other published artifacts: Dublin Core and BIBO. Dublin Core is actually the de-facto standard for metadata for just about anything that can be published. BIBO, the bibliographic ontology, is more specialised (and actually rely on both Dublin Core and FOAF) to represents in particular academic publications.

Other vocabularies used include generic “representation languages” such as RDF, RDFS, OWL and SKOS (often used to represent topics), as well as specific vocabularies related to the description of multimedia resources, events and places (including building, addresses and geo-location).

Common Classes

At a more granular level, it is interesting to look at the types of entities that can be found in the considered datasets. The chart above shows the classes that are used by at least 2 datasets. This confirms in particular the strong focus on people and bibliographic/learning resources (Article, Book, Document, Thesis, Podcast, Recording, Image, Patent, Report, Slideshow).

In second place come information about educational institutions as organisations and physical places (Organization, Institution, Building, Address, VCard).

Besides generic, language-level classes other areas such as events, courses, vacancies, etc. tend to be only considered by a very small number of datasets.

Common Properties

Finally, going a step further in granularity, we look through the chart above at the way common types of entities are represented. This chart show the properties used by more than 3 datasets. Once again, besides generic properties, the focus on people (name) and media/bibliographic resources (title, date, subject) is obvious, especially with properties connecting the 2 (contributor, homepage).

The representation of institutions as physically located places is also clearly reflected here (lat, long, postal-code, street-address, adr).

Doing More with the Collected Data

Of course, the considered datasets only represent a small sample, and ideally, we could draw some more definitive conclusions as the number of education-related datasets grows and are included. Indeed, in order to realise the analysis in this post, we created a script that generates VOID-based descriptions of the datasets. The created descriptions are available on a public SPARQL endpoint which will be extended as we find more datasets to include. Please let us know if there are datasets you would like to see taken into account. The charts above are dynamically generated out of SPARQL query to the aforementioned SPARQL endpoint.

Also, we will look at reflecting the elements discussed here on the vocabulary page of LinkedUniversities.org. The nice thing about having a SPARQL endpoint for the collected data is that it will make it easy to create a simple tool to explore the “Vocabulary Space” of educational datasets. This might appear useful as well as a way provide federated querying services for common types of entities (see this recent paper about using VOID for doing that), which might end-up being a useful feature for the recently launched data.ac.uk initiative (?) Another interesting thing to do would be to apply a tiny bit of data-mining to check for example what elements tend to appear together, and see if there are common patterns in the use of some vocabularies.