Opening public sector information has recently become a trend in many countries around the world. Online government data catalogues with national, regional or local scope act as one-stop data portals providing descriptions of available government datasets. These catalogues though remain isolated. Potential benefits from federating geographically overlapping or thematically complementary catalogues are not realized. We propose an RDF Schema vocabulary as an interchange format among data catalogues and as a way of bringing them into the Web of Linked Data, where they can enjoy interoperability among themselves and with other deployed datasets. The vocabulary's design was informed by a survey of seven data catalogues from five different countries, and has been verified by unifying four data catalogues to allow cross-catalogue queries and browsing.

We tackle the challenges involved in converting raw government data into high-quality Linked Government Data (LGD). Our approach is centred around the idea of self-service LGD which shifts the burden of Linked Data conversion towards the data consumer. The selfservice LGD is supported by a publishing pipeline that also enables sharing the results with sufficient provenance information. We describe how the publishing pipeline was applied to a local government catalogue in Ireland resulting in a significant amount of Linked Data published.

Open Government Data initiatives in the US, UK and elsewhere have made large amounts of raw data available to the public on the Web. There is enormous potential in applying Linked Data principles to these datasets. This potential currently remains largely untapped because governments lack the resources required to convert from raw data to highquality Linked Data on a large scale. We present a "selfservice" approach to this problem: By connecting a powerful Gridworks-based data workbench application directly to data catalogs, via a standard Data Catalog Vocabulary, data professionals outside of government can contribute to the Linked Data conversion process, thus obtaining data for their own needs and benefiting the larger Linked Government Data effort.

One of government's responsibilities is the provision of public services to its citizens, for example, education, health, transportation, and social services. Additionally, with the explosion of the Internet in the past 20 years, many citizens have moved online as their main method of communication and learning. Therefore, a logical step for governments is to move the provision of public services online. However, public services have a complex structure and may span across multiple, disparate public agencies. Moreover, the legislation that governs a public service is usually difficult for a layman to understand. Despite this, governments have created online portals to enable citizens to find out about and utilise specific public services. While this is positive, most portals fail to engage citizens because they do not manage to hide the complexity of public services from users. Many also fail to address the specific needs of users, providing instead only information about the most general use-case. In this paper we present the Semantic Public Service Portal (S-PSP), which structures and stores detailed public-services semantically, so that they may be presented to citizens on-demand in a relevant, yet uncomplicated, manner. This ontologybased approach enables automated and logical decision-making to take place semantically in the application layer of the portal, while the user remains blissfully unaware of its complexities. An additional benefit of this approach is that the eligibility of a citizen for a particular public service may be identified early. The S-PSP provides a rich, structured and personalised public service description to the citizen, with which he/she can consume the public service as directed. In this paper, a use-case of the S-PSP in a rural community in Greece is described, demonstrating how its use can directly reduce the administrative burden on a citizen, in this case is a rural Small and Medium Enterprise (SME).

The recent big data movement resulted in a surge of activity on layering declarative languages on top of distributed computation platforms. In the Semantic Web realm, this surge of analytics languages was not reflected despite the significant growth in the available RDF data. Consequently, when analysing large RDF datasets, users are left with two main options: using SPARQL or using an existing non-RDF-specific big data language, both with its own limitations. The pure declarative nature of SPARQL and the high cost of evaluation can be limiting in some scenarios. On the other hand, existing big data languages are designed mainly for tabular data and, therefore, applying them to RDF data results in verbose, unreadable, and sometimes inefficient scripts. In this paper, we introduce SYRql, a dataflow language designed to process RDF data at a large scale. SYRql blends concepts from both SPARQL and existing big data languages. We formally define a closed algebra that underlies SYRql and discuss its properties and some unique optimisation opportunities this algebra provides. Furthermore, we describe an implementation that translates SYRql scripts into a series of MapReduce jobs and compare the performance to other big data processing languages.

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