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.

Indeed, with support from the Open University’s “Open Media Unit”, we built an application that can semantically analyse the textual content of online resources and match it agains semantically indexed Open University content (OpenLearn Units and Podcasts at the moment) . The result (implemented as a set of REST services, some Javascript and a bookmarklet) is, if I might say so myself, super cool. It’s called:

(and yes, we probably should have put more effort in choosing the name).

The whole thing is pretty much a combination of linked data and information retrieval technologies. The Open University resources are crawled through data.open.ac.uk, analysed using DBPedia Spotlight and indexed using Apache Lucene. A BBC programme page used as a starting point would pretty much go through the same process, using the RDF description of the programme from the BBC website, analysing the textual components and matching the results to indexed resources. Because we use DBpedia Spotlight, the resources are described (and indexed) based on DBpedia entities, which allows us to semantically characterise their overlap, based on the links between common entities. It also makes it possible for the user to customise the search process based on his/her own interests.

You can look at the ontology to get an idea of the data model used there, send a couple of SPARQL queries to `explore’ the data, look at example objects. etc. We also provide example SPARQL queries to help people getting the point of our datasets. Of course, not everybody is proficient enough in SPARQL, RDF-S and OWL to really get it using this sort of clues. Also, datasets might be heterogeneous in the representation of objects, in the distribution of values, or simply very big and broad.

To help people who don’t necessarily know/care about SPARQL `getting into’ a complex dataset, we developed a system (whatoask) that automatically extract a set of questions that a dataset is good at answering. The technical aspects of realising that are a tiny bit sophisticated (i.e., it uses formal concept analysis) and are detailed in a paper I will present next week at the K-CAP conference. What is interesting however is how such a technique can provide a navigation and querying interface of top of a linked dataset, providing a simple overview of the data and a way to drill down to particular areas of interest. In essence, it can be seen as an FAQ for a dataset, not presenting frequently asked questions, but the questions the dataset is specially good at answering.

What the tool does is creating a hierarchy of all the simple questions an RDF dataset can answer, and presents to the user a subset that, according to a set of metrics described in the paper, are believed to be more likely of interest. The questions are displayed in a pseudo natural language, in a format where for example “What are the (Person/*) that (knows Tom) and that (KMi hasEmployee)?” can be interpreted as the question “What are the people who know Tom and are employed in KMi?”. Questions can be selected, and displayed with their answers, and the question hierarchy can be navigated, selecting more specific and more general questions than the selected one.

To clarify what that means, let’s look at what it does on the data.open.ac.uk OpenLearn dataset. The initial screen shows a list of questions, the first one (“What are the (Document/*/OpenLearnUnit) that (subject Concept, relatesToCourse Course, relatesToCourse Module)?”, i.e., “What are the OpenLearn Units that are related to courses and have a topic?”) being selected. More general and more specific questions are also shown, such as “What are the OpenLearn Units that have a topic?” (more general) and “What are the OpenLearn Units that relate to a course and have for topic `Education Research’?” (more specific).

We can select alternative questions, such as the second in the list — “What are the OpenLearn Units in english distributed under a creative commons licence and that talk about Science?”, obtain a new list of answers (quite a few), as well as more general and more specific questions. We can then specialise the question to “What are the OpenLearn Unit in english under a CC licence that talk about science and family?” and carry-on with a more general question looking at the `family topic’ without science, to finally ask “What are the OpenLearn units about family?” (independently of the licence and language).

As can be seen from the example, the system is not meant for people who know in advance what they want to ask, but to provide a level of serendipitous navigation amongst the queries the dataset can answer, with the goal of giving a general overview of what the dataset is about and what it can be used for. The same demo is also available using the set of reading experiences from the RED dataset and the datasets regarding buildings and places at the OU. The interface is not the most straightforward at the moment, but we are thinking about ways by which the functionalities of the system could be integrated in a more compelling manner, as a basic `presentation’ layer on top of a linked dataset.

However, these data was not very well connected. We used links to the postcode unit descriptions from the address to the ordnance survey dataset, giving us an general view on the locations of buildings (and so allowing us to build a very crude map of OU buildings in the UK), but we didn’t have precise locations of buildings. We also couldn’t relate the buildings with events (e.g., tutorials), people (through their workplace, attendance, etc.)

We therefore decided to build an application to not only use these data, but also create some of these missing relations, and specially, to allow OU users to connect to the data.

The application is called wayOU, for “where are you in the OU?”. It can be used to “check-in” at specific locations indicating the “reason” for attending these locations, to keep track of the different places where the user has been, declare the current location as his / her workplace, as well as to connect to their network at the Open University, in terms of the people they share activities with. The video below explains the principle of the application better than I can do with text.

The application is now being tested and is made available for download (see QR code below – without guaranty that it will actually work) on data.open.ac.uk. Fouad is going to demonstrate it next week at the Extended Semantic Web Conference next week (see the abstract of the demonstration), and (perhaps more importantly) the sources of this first release are available in our code base.

The winning application of the KMi Linked Data Application Competition has attracted the interest of the ROLE project (Responsive Open Learning Environments – www.role-project.eu). The OpenLearn Linked Data application was originally developed by Fouad Zablith as a showcase of querying data.open.ac.uk for educational resources related with OpenLearn courses. I have now transformed this application into a widget that can be directly embedded into any OpenLearn course as a Moodle block. The widget displays a list of Open University courses, iTunesU podcasts, as well as OpenLearn tags related to the course that the user is currently viewing. As data.open.ac.uk is constantly growing by integrating metadata from more repositories, the widget will also be extended with recommendations about educational resources of additional types. You can try out the current release by logging in as guest at: http://projects.kmi.open.ac.uk/role/moodle/course/view.php?id=3.

This widget is part of the widget bundles developed by ROLE for providing self-regulated learning support. ROLE is aiming at empowering learners for lifelong and personalised learning within a responsive open learning environment. OpenLearn comprises one of the project’s test-beds concerning the transition from formal learning towards informal learning, where the learner is in control of the whole learning process. For more information about the learning technologies developed so far by ROLE, please visit the Showcase Platform (http://www.role-showcase.eu/).

That’s when we thought of organising a linked data application competition in KMi. KMi is full of very smart people, researchers and PhD students with the skills, knowledge and energy to build this sort of apps: lightweight, web or mobile applications to demonstrate one specific aspect and one specific use of the Open University’s linked data. I’m not going to give all the details of the way the competition was organised. We received four incredibly interesting applications (the promise of winning an iPad might have helped). This four applications are now featured on the brand new Data.open.ac.uk Application Page together with other applications currently being developed.

So, congratulations to our winners! The choice was really difficult (and you might not agree with it), as the applications described below are all great examples of the many things that can be achieved through opening up and linking university data.

The Winner: OpenLearn Linked Data (Fouad Zablith)

OpenLearn Linked Data makes use of data from data.open.ac.uk to suggest courses, podcasts and other OpenLearn units that relate to an OpenLearn Unit. The application takes the form of a bookmarklet that, when triggered while browsing the webpage of an OpenLearn unit, will add to the page small windows with links to the relevant course in Study at the OU, to podcasts from the OU podcast repositories and units from OpenLearn that share a common tag.

The great thing about this application is that it addresses scenarios directly relevant to students, prospective students and users of OpenLearn in general. It very naturally exploits the way linked data removes the boundaries that exist between different systems within the Open University, without having to change or integrate these systems.

Second Place: OU Expert Search (Miriam Fernandez)

The OU Expert Search system (accessible inside the OU network only) allows users to find academics at the Open University who are experts in a given domain, providing a ranked list of experts based in particular on their research publications. It uses information about publications in ORO and computes complex measures to provide a ranking of the people who are most likely to be experts in the given domain. It also integrates data obtained from the staff directory of the Open University to provide contact details for the people in the system.

Here as well the strong point of the application is its apparent simplicity. It is very easy to use and has been applied already for example to find Open University experts on Volcanoes (see Stuart’s blog post). Expert search is a complex task, and OU Expert Search, through the use of linked data, makes it look rather simple.

OUExperts (Vlad Tanasescu)

OUExperts is a mobile (android) application to find Open Univeristy experts in a given domain, and connect to their social network. Similarly to the OU Expert Search application, it relies on information related to the scientific publications of OU researchers, as available in ORO. It also finds synonyms of the given keywords, and tries to connect to the pages of the listed researchers.

The interesting aspect of OUExperts, apart from being a mobile application, is the clever attempt to connect to social networking website, so that it is not only possible to find an expert, but also to connect to them on Facebook or LinkedIn.

Buddy Study (Matthew Rowe)

Buddy Study suggests potential contacts and Open University courses to follow for students, based on the analysis of the topics in the user’s Facebook page. The application attempts to extract from the user’s Facebook page prominent topics, which are then matched to the interests of other people, and to the topics covered by courses at the Open University.

In this case, it is the social aspect of a user’s presence online which is used to create connections into the data from the Open University, creating easily accessible entry points to the data.