C. Rensing et al. (Eds.): EC-TEL 2014, LNCS 8719, pp. 558–559, 2014. © Springer International Publishing Switzerland 2014

EAGLE – Open Data and Linked Data Architecture of an Enhanced Government Learning Platform*

Violeta Damjanovic1, Dietmar Glachs1, Nikolay Tcholtchev2, Eric Ras3, and Eric Tobias3

1 Salzburg Research, Austria 2 Fraunhofer Institute for Open Communication Systems, Germany

3 Public Research Centre Henri Tudor, Luxembourg {violeta.damjanovic,dietmar.glachs}@salzburgresearch.at,

nikolay.tcholtchev@fokus.fraunhofer.de, {eric.ras,eric.tobias}@tudor.lu

Abstract. We present the architecture of an Open Data and Linked Data platform for "EnhAnced Government LEarning” (EAGLE), which aim is to help local governments to keep up with fast-changing trends in public administration (PA) by adopting technology-enhanced learning (TEL) methods. EAGLE uses Linked Open Data tools: Apache Marmotta and Apache Stanbol.

Keywords: Linked Open Data, semantic enhancement, government learning.

1 Introduction

In our pre-study on learning in public administration (PA), in rural local government, conducted in Luxembourg, Montenegro, Germany, Austria, and Ireland in 2012/13, the following problems have been identified: “Lack of timely access to learning content, lack of well-established learning processes at the workplace, lack of change management procedures […]” followed by the fact that “ […] in 2012, 86 regulations and 67 laws were changed in Germany; 54 regulations and 33 laws were already changed in 2013.” Hence, our motivation is to create a learning platform, which eases adoption of changing trends in PA. Herein, we present the architecture of the EAGLE Open Data and Linked Data (LD) learning platform, which builds on top of existing open source tools and frameworks, and develops a novel search, presentation and navigation strategy for education in PA.

2 EAGLE Architecture

The EAGLE learning platform architecture follows an open source policy, and reuses existing open source tools and components, such as relevant public sector datasets developed in LATC and LOD2 projects, as well as LD generated in different PA * The research leading to these results has received funding from the EC FP7 (FP7/2007-2013)

under grant agreement N° 619347.

EAGLE – Open Data and Linked Data Architecture 559

context. It builds on top of several existing tools for the creation, linking, and publication of LD, and integrates open source tools: Apache Stanbol, which allows for semantic analysis, and Apache Marmotta, a LD platform [1]. The aim of the EAGLE architecture is to support aggregation, refinement, and usage of various Open Educational Resources (OER). The architecture specifies a set of components (see Figure 1): (i) OER Linked Data, meant for information management, (ii) OER Data Registry, for data management, based on the OpenData CKAN standard, and (iii) Automatic Item Generation (AIG), for self-assessment of information literacy in PA.

Fig. 1. High-level architecture of the EAGLE Open Data & Linked Data learning platform

To realise OERs as LD, we apply the following steps: (i) Data modelling: we develop an OER ontology to be used in PA; (ii) Data reusing: we either import structured data from the existing LD sources, or transform non-structured sources first into RDF, then align structured RDF data to the OER ontology; (iii) Data interlinking: we perform semantic lifting and content enhancement of the data to be stored, or modified; (iv) Data storage: all content enhancements and data aligned to the OER ontology are stored in the OER Data Registry; and (v) Data publication: the data from the EAGLE platform are available to EAGLE (as well as LD-) services, either via SPARQL or via RESTful services.

3 Conclusions and Future Work

Incorporating LD and Semantic Web technologies for learning in PA offers several benefits, such as collaborative creation and sharing of knowledge, improved interactivity due to concept-based navigation and search capabilities. In EAGLE, the major technical challenge will be to provide a smooth integration of the existing open source platforms/tools with PA services. One of the first steps is the enhancement of the OER metadata scheme to support CKAN for the OER Data Registry component.

References

1. Kurz, T., Güntner, G., Damjanovic, V., Schaffert, S., Fernandez, M.: Semantic Enhancement for Media Asset Management Systems: Integrating the Red Bull Content Pool into the Web of Data. In: Multimedia Tools and Applications, MTAP (2012)