The Open University Linked Data - data.open.ac.uk

The article reports on the evolution of data.open.ac.uk, the Linked Open Data platform of the Open University, from a research experiment to a data hub for the open content of the University. Entirely based on Semantic Web technologies (RDF and the Linked Data principles), data.open.ac.uk is used to curate, publish and access data about academic degree qualifications, courses, scholarly publications and open educational resources of the University. It exposes a SPARQL endpoint and several other services to support developers, including queries stored server-side and entity lookup using known identifers such as course codes and YouTube video IDs. The platform is now a key information service at the Open University, with several core systems and websites exploiting linked data through data.open.ac.uk. Through these applications, data.open.ac.uk is now fulfilling a key role in the overall data infrastructure of the university, and in establishing connections with other educational institutions and information providers

Consuming Linked Data within a Large Educational Organization

Large universities tend to spread their services across several departments to serve their substantial student base. It is very common for this to result in developing different systems, which end up in creating many disconnected data silos within the organization. Data isolation is one of the main bottlenecks that prevent unlocking the full potential behind exploiting such data, to provide a better experience at the level of application deployment and data analysis. The Open University is in the process of connecting their data silos by relying on the Linked Data principles within the LUCERO project. We discuss in this paper three use-cases through which we consume Linked Data produced at the Open University: (1) a student services use-case showing how we exploit data connections to deliver learning material related to courses through the university's main course information website; (2) a mobile course application that enables students to easily explore courses by subject, qualification or research topic; and (3) a Leanback TV service that provides students the ability to watch, with a degree of control, a set of podcasts grouped in different channels. Through these use cases, we highlight in this paper the advantages and effects of consuming Linked Data within an organization

A BASILar Approach for Building Web APIs on top of SPARQL Endpoints

The heterogeneity of methods and technologies to publish open data is still an issue to develop distributed systems on the Web. On the one hand, Web APIs, the most popular approach to offer data services, implement REST principles, which focus on addressing loose coupling and interoperability issues. On the other hand, Linked Data, available through SPARQL endpoints, focus on data integration between distributed data sources. The paper proposes BASIL, an approach to build Web APIs on top of SPARQL endpoints, in order to benefit of the advantages from both Web APIs and Linked Data approaches. Compared to similar solution, BASIL aims on minimising the learning curve for users to promote its adoption. The main feature of BASIL is a simple API that does not introduce new specifications, formalisms and technologies for users that belong to both Web APIs and Linked Data communities

Linking Data Across Universities: An Integrated Video Lectures Dataset

This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1, or the W3C Ontology for Media Resources 2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme

The OU Linked Open Data: production and consumption

The aim of this paper is to introduce the current efforts toward the release and exploitation of The Open University's (OU) Linked Open Data (LOD). We introduce the work that has been done within the LUCERO project in order to select, extract and structure subsets of information contained within the OU data sources and migrate and expose this information as part of the LOD cloud. To show the potential of such exposure we also introduce three different prototypes that exploit this new educational resource: (1) the OU expert search system, a tool focused on fnding the best experts for a certain topic within the OU staff; (2) the Buddy Study system, a tool that relies on Facebook information to identify common interest among friends and recommend potential courses within the OU that `buddies' can study together, and; (3) Linked OpenLearn, an application that enables exploring linked courses, Podcasts and tags to OpenLearn units. Its aim is to enhance the browsing experience for students, by detecting relevant educational resources on fly while reading an OpenLearn unit

Update of time-invalid information in Knowledge Bases through Mobile Agents

In this paper, we investigate the use of a mobile, autonomous agent to update knowledge bases containing statements that lose validity with time. This constitutes a key issue in terms of knowledge acquisition and representation, because dynamic data need to be constantly re-evaluated to allow reasoning. We focus on the way to represent the time- validity of statements in a knowledge base, and on the use of a mobile agent to update time-invalid statements while planning for “information freshness” as the main objective. We propose to use Semantic Web standards, namely the RDF model and the SPARQL query language, to represent time-validity of information and decide how long this will be considered valid. Using such a representation, a plan is created for the agent to update the knowledge, focusing mostly on guaranteeing the time-validity of the information collected. To show the feasibility of our approach and discuss its limitations, we test its implementation on scenarios in the working environment of our research lab, where an autonomous robot is used to sense temperature, humidity, wifi signal and number of people on demand, updating the knowledge base with time- valid information

STELLAR (Semantic Technologies Enhancing the Lifecycle of Learning Resources): Jisc Final Report

[Project Summary] As one of the earliest distance learning providers The Open University (OU) has a rich heritage of archived learning materials. An ever increasing amount of that is in digital form and is being deposited with the University Archive. This growth has been driven by digitisation activity from projects such as AVA (Access to Video Assets) and the Fedora-based Open University Digital Library ‘a place to discover digital and digitised archival content from the OU Library, from videos and images to digitised documents’. Other digital content is being captured from web archiving activities, such as work to preserve Moodle Virtual Learning Environment course websites. An evidence based understanding is required to inform digital preservation policies, curation strategy and investment in digital library development. Following the Pre-enhancement, Enhancement and Post-enhancement methodology set out by Jisc, STELLAR adopted the model of a balanced scorecard to ascertain the value ascribed to the non-current learning materials. Four aspects were considered: Personal and professional perspectives of value; Value to the Higher Educational and academic communities; Value to internal processes and cultures; Financial perspectives of value. The outcomes of the survey indicated that stakeholders place a high value on the materials, and that they perceived them to have value in all areas evaluated. Three OU courses were chosen from the digital library for the transformation stage. These materials were enhanced and transformed into RDF, a process that required more extensive metadata expertise and effort than was expected. Following enhancement the RDF was accessed through a tool called DiscOU, created by a member of the project team from the OU’s Knowledge Media Institute. DiscOU uses both linked data and a semantic meaning engine to analyse the meaning of the text in a search query. This is matched against the meaning of the content derived from an index of the full-text of the digital library content. In the final stage stakeholders were asked through a survey and series of workshops to use the DiscOU proof-of-concept tool to assess their perception of the value of this transformation. This has revealed that overall, academics and other stakeholders in the university do believe that the value of the selected materials was positively impacted by the application of semantic technologies

Crowdsourcing Linked Data on listening experiences through reuse and enhancement of library data

Research has approached the practice of musical reception in a multitude of ways, such as the analysis of professional critique, sales figures and psychological processes activated by the act of listening. Studies in the Humanities, on the other hand, have been hindered by the lack of structured evidence of actual experiences of listening as reported by the listeners themselves, a concern that was voiced since the early Web era. It was however assumed that such evidence existed, albeit in pure textual form, but could not be leveraged until it was digitised and aggregated. The Listening Experience Database (LED) responds to this research need by providing a centralised hub for evidence of listening in the literature. Not only does LED support search and reuse across nearly 10,000 records, but it also provides machine-readable structured data of the knowledge around the contexts of listening. To take advantage of the mass of formal knowledge that already exists on the Web concerning these contexts, the entire framework adopts Linked Data principles and technologies. This also allows LED to directly reuse open data from the British Library for the source documentation that is already published. Reused data are re-published as open data with enhancements obtained by expanding over the model of the original data, such as the partitioning of published books and collections into individual stand-alone documents. The database was populated through crowdsourcing and seamlessly incorporates data reuse from the very early data entry phases. As the sources of the evidence often contain vague, fragmentary of uncertain information, facilities were put in place to generate structured data out of such fuzziness. Alongside elaborating on these functionalities, this article provides insights into the most recent features of the latest instalment of the dataset and portal, such as the interlinking with the MusicBrainz database, the relaxation of geographical input constraints through text mining, and the plotting of key locations in an interactive geographical browser

Knowledge Components and Methods for Policy Propagation in Data Flows

Data-oriented systems and applications are at the centre of current developments of the World Wide Web (WWW). On the Web of Data (WoD), information sources can be accessed and processed for many purposes. Users need to be aware of any licences or terms of use, which are associated with the data sources they want to use. Conversely, publishers need support in assigning the appropriate policies alongside the data they distribute. In this work, we tackle the problem of policy propagation in data flows - an expression that refers to the way data is consumed, manipulated and produced within processes. We pose the question of what kind of components are required, and how they can be acquired, managed, and deployed, to support users on deciding what policies propagate to the output of a data-intensive system from the ones associated with its input. We observe three scenarios: applications of the Semantic Web, workflow reuse in Open Science, and the exploitation of urban data in City Data Hubs. Starting from the analysis of Semantic Web applications, we propose a data-centric approach to semantically describe processes as data flows: the Datanode ontology, which comprises a hierarchy of the possible relations between data objects. By means of Policy Propagation Rules, it is possible to link data flow steps and policies derivable from semantic descriptions of data licences. We show how these components can be designed, how they can be effectively managed, and how to reason efficiently with them. In a second phase, the developed components are verified using a Smart City Data Hub as a case study, where we developed an end-to-end solution for policy propagation. Finally, we evaluate our approach and report on a user study aimed at assessing both the quality and the value of the proposed solution

Using Linked Data in Learning Analytics

Learning Analytics has a lot to do with data, and the way to make sense of raw data in terms of the learner’s experience, behaviour and knowledge. In this article, we argue about the need for a closer relationship between the field of Learning Analytics and the one of Linked Data, which in our view constitutes an ideal data management layer for Learning Analytics. Based on our experience with organising the “Using Linked Data in Learning Analytics” tutorial at the Learning Analytics and Knowledge conference, we discuss the existing trends in the use of linked data and semantic web technologies, in general in education and in learning analytics specifically. We find that the emerging connections between the two fields are still, at the time of writing, much less prominent than one would expect considering the complementary nature of the considered technologies and practices. We therefore argue that specific efforts, somehow materialised through the tutorial and the work in the LinkedUp support action, are needed to ensure the realisation of the potential cross-benefits that combining Learning Analytics and Linked Data research could bring.LinkedU