This paper presents our approach to designing and developing the EAGLE (EnhAnced Government Learning) platform and its Open Data and Linked Data features. The aim of the platform is for local governments in rural areas to adopt Technology-Enhanced Learning (TEL) solutions as an effective way to keep up with the fast pace of innovation, which is affecting public administration in the EU. EAGLE is based on open source tools, such as Apache Marmotta (Linked Data Platform) and Apache Stanbol (semantic enhancement engine). It uses Linked Data as an integration technology. We present three steps to our approach: (i) data integration from various government and/or learning data sources, (ii) semantic data enhancement, and (iii) publishing the enhanced data as Linked Data. These steps lead towards creation and deployment of services tailored to support extensive learning for public administration in rural areas, employing assessment technologies, change management methods, and cross-device learning strategies.
A new transformational trend in education, known as “open education”, relies on (i) novel models for delivering learning content such as Open Educational Resources (OER) e.g. through Massive Open Online Courses (MOOC), etc., (ii) the use of stateof-the-art technologies for sharing, publishing and connecting information globally, (iii) massive adoption of novel online and distance learning models in schools, but also by leading business and government institutions. Public administration (PA) typically has to cope with a large amount of heterogeneous data, which is either distributed by various users and topics, or triggered by changes in current regulations and laws. Today, many rural regions and their local governments are greatly affected by globalization and economic developments all requiring new policies addressing agricultural, environmental, or tourism concerns (e.g. services such as supply of drinking water, renewable energy, biofuels, electricity production from biomass, biodiversity in agriculture, rural tourism, transportation, etc.)
Although learning in PA is a fairly new field, several different approaches have
already been tested; for example, using professional learning profiles [
In this paper, we present our approach to design and develop the EAGLE (EnhAnced Government LEarning) learning platform. Section 2 presents related work in Open Data and Linked Data technologies for data integration in the field of open education. In addition, it discusses our motivation to develop a learning platform to foster adoption of trends in local (rural) government policies. Section 3 shows the architecture of the EAGLE platform. Section 4 presents our approach to integrate, enhance and publish governmental data for educational purposes. The EAGLE platform encourages the development of novel educational services, which need to be tailored to specific government learning needs. Some of these services are mentioned in Section 5, although the full set of EAGLE learning services is outside of the scope of this paper. Section 5 presents next steps and concludes the paper.
Related work. Linked Data Platform 1.0, commonly abbreviated as LDP [
The W3C’s Government Linked Data (GLD) Working Group2 provides standards
and information, which enable governments around the world to publish their data as
LD. Another W3C eGovernment Interest Group is meant for broad community
discussion of governmental use on the Web. One of the best LD practices in
government so far is the governmental data portal in the UK3. Recently, Google and
Microsoft have started to employ LD as an integral part of their offerings [
Motivation of our work. To understand the main directions of learning challenges in local government, we have conducted an interview-based pre-study in Luxembourg, Montenegro, Germany, Austria, and Ireland in 2012/13. This pre-study shows that specific regional government knowledge often fails to find its way to the learning materials. Offline courses are not sustainable and updating the material is cumbersome and expensive. Educational training is mostly delivered face-to-face. It cannot respond to short-term learning needs, and requires absence from the workplace over a considerable amount of time. Learning materials are available only to participants. Therefore, our motivation in EAGLE is to apply modern search, presentation and navigation strategies to support education in local government. We reuse public sector datasets developed in EU projects, such as LATC6, as well as educational datasets, e.g. datasets generated in the OpenScout7 and the LinkedUp projects. 3
The aim of the EAGLE architecture [
The main objective of the EAGLE project is to provide novel search, presentation and navigation services, built on top of the EAGLE platform, supporting TEL in local governments, with end-users from Germany, Ireland, Luxembourg and Montenegro. In this paper, we present the EAGLE information management functionality through exposing learning objects to the Web of Data. For the sake of conciseness, we present only three steps in publishing governmental data: 1. Data integration: We either import structured data from the existing LD sources or transform other non-LD sources into LD-RDF triples via data source adapters 2. Data enhancement: Semantic lifting and content enhancement of the data; 3. Data publishing: The data stored in OER Registry are becoming, either via
SPARQL or LDP, available to EAGLE and/or the LD cloud.
EAGLE Data Integration: EAGLE enables seamless (re)use of data coming from (i) LD resources (e.g. Freebase), and/or (ii) non-LD sources, such as OER repositories (e.g. EUR-Lex11 with the access to European law). This includes supporting data (e.g. Thesauri) for interlinking OER’s with the Web of Data, as well as with open government data for education purposes. Fig. 2 shows the integration aspects of linked resources stored on remote servers in the LD cloud. EAGLE uses the built-in LDCache component of Apache Marmotta, which transparently harvests LD resources and makes them locally available for querying and indexing. LDCache also enables data interlinking with external LD sources, e.g., legal datasets from the LATC project. One of our tasks in EAGLE is to develop dedicated LD connectors for nonLD learning platforms, such as moodle and OpenScout, enabling these platforms to be accessible as LD sources via EAGLE. Governmental datasets, e.g. Légilux, will be also integrated and used to support learning processes. EAGLE enforces the usage of standard metadata formats, e.g. IMS Metadata for IEEE 1484.12.1-2002 standard for Learning Object Metadata (LOM)12. 11 EUR-Lex data portal: http://eur-lex.europa.eu/ 12 LOM website: http://ltsc.ieee.org/wg12/20020612-Final-LOM-Draft.html
EAGLE Data Enhancement: To achieve data harvesting and interlinking of data
from external sources, we perform semantic interlinking by using the Apache Stanbol
semantic enhancement engine. Apache Stanbol allows for a series of extractors and
annotators to be used in a fashion like UNIX-pipes, based on specific content features,
e.g. MIME type or language. In such a way, various OERs, either retrieved from
external sources and/or stored in EAGLE OER Registry, will be annotated and
interlinked with concepts from the Web of Data [
EAGLE Data Publication: Besides the Apache Stanbol enhancement engine, the
EAGLE learning platform incorporates Apache Marmotta, a LDP compliant platform
[
Linked Data technology has the potential to solve some important problems that have frustrated the IT industry for many years. This requires a much richer body of knowledge to be communicated on how to exploit these technologies in various organizational habitats. It also requires that present gaps in the LD standards, as well as some legal barriers to commercialization of LD (i.e. gaps in a range from the missing licensing information to restrictive licenses prohibiting commercial usage of LD) need to be properly addressed.
The overall implications of exposing various datasets related to the local governmental learning to the Web of Data are significant, offering benefits such as: collaborative creation and sharing of local governmental knowledge on the Web of Data; semantically and LD-based enhanced learning opportunities; improved interactivity due to concept-based navigation and search capabilities; improved findability of data when all alternative terms, synonyms, acronyms or jargon can be used to find or match ideas or concepts, etc.
One of the major technical challenges for EAGLE is to provide a smooth
integration of the existing open source platforms, tools and services into the EAGLE
Open Data and Linked Data learning platform. In that context, one of the first steps is
the enhancement of the OER metadata scheme to support CKAN for the OER
Registry component (Fig. 1). In parallel the LD connectors to existing non-LD OER
repositories will be developed to enable data reuse within the EAGLE platform. In
addition, an OER ontology focusing on public administration will be developed,
considering the current OER-CC ontology presented in [
The EAGLE Open Source and Linked Data local governmental learning platform follows the emerging W3C LDP recommendations, and will be the first LD platform for OER-based learning in public administration in the EU.
Acknowledgements. The research leading to these results has received funding from the EC FP7 (FP7/2007-2013) under grant agreement N° 619347 (project website: http://www.eagle-learning.eu/).