As the Web of Data is growing at an ever increasing speed, the lack of reliable query solutions for live public data becomes apparent. sparql implementations have matured and deliver impressive performance for public sparql endpoints, yet poor availability-especially under high loads-prevents their use in real-world applications. We propose to tackle this availability problem by defining triple pattern fragments, a specific kind of Linked Data Fragments that enable low-cost publication of queryable data by moving intelligence from the server to the client. This paper formalizes the Linked Data Fragments concept, introduces a client-side sparql query processing algorithm that uses a dynamic iterator pipeline, and verifies servers' availability under load. The results indicate that, at the cost of lower performance, query techniques with triple pattern fragments lead to high availability, thereby allowing for reliable applications on top of public, queryable Linked Data.

Abstract. rdf dataset quality assessment is currently performed primarily after data is published. However, there is neither a systematic way to incorporate its results into the dataset nor the assessment into the publishing workflow. Adjustments are manually -but rarely-applied. Nevertheless, the root of the violations which often derive from the mappings that specify how the rdf dataset will be generated, is not identified. We suggest an incremental, iterative and uniform validation workflow for rdf datasets stemming originally from (semi-)structured data (e.g., csv, xml, json). In this work, we focus on assessing and improving their mappings. We incorporate (i) a test-driven approach for assessing the mappings instead of the rdf dataset itself, as mappings reflect how the dataset will be formed when generated; and (ii) perform semi-automatic mapping refinements based on the results of the quality assessment. The proposed workflow is applied to diverse cases, e.g., large, crowdsourced datasets such as dbpedia, or newly generated, such as iLastic. Our evaluation indicates the efficiency of our workflow, as it significantly improves the overall quality of an rdf dataset in the observed cases.

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Abstract. Mapping languages allow us to define how Linked Data is generated from raw data, but only if the raw data values can be used as is to form the desired Linked Data. Since complex data transformations remain out of scope for mapping languages, these steps are often implemented as custom solutions, or with systems separate from the mapping process. The former data transformations remain case-specific, often coupled with the mapping, whereas the latter are not reusable across systems. In this paper, we propose an approach where data transformations (i) are defined declaratively and (ii) are aligned with the mapping languages. We employ an alignment of data transformations described using the Function Ontology (fno) and mapping of data to Linked Data described using the rdf Mapping Language (rml). We validate that our approach can map and transform dbpedia in a declaratively defined and aligned way. Our approach is not case-specific: data transformations are independent of their implementation and thus interoperable, while the functions are decoupled and reusable. This allows developers to improve the generation framework, whilst contributors can focus on the actual Linked Data, as there are no more dependencies, neither between the transformations and the generation framework nor their implementations.

Abstract. Applications built on top of the Semantic Web are emerging as a novel solution in different areas, such as decision making and route planning. However, to connect results of these solutions -i.e., the semantically annotated data -with real-world applications, this semantic data needs to be connected to actionable events. A lot of work has been done (both semantically as non-semantically) to describe and define Web services, but there is still a gap on a more abstract level, i.e., describing interfaces independent of the technology used. In this paper, we present a data model, specification, and ontology to semantically declare and describe functions independently of the used technology. This way, we can declare and use actionable events in semantic applications, without restricting ourselves to programming language-dependent implementations. The ontology allows for extensions, and is proposed as a possible solution for semantic applications in various domains.

Taking the region of Flanders in Belgium as a case study, this article reflects on how smart cities initiated a grassroots initiative on data interoperability. We observe that cities are struggling due to the fragmentation of data and services across different governmental levels. This may cause frustrations in the everyday life of citizens as they expect a coherent user experience. Our research question considers the relationship between individual characteristics of decision makers and their intention to use data standards. We identified criteria for implementing data standards in the public sector by analysing the factors that affect the adoption of data governance, based on the Technology Readiness and Acceptance Model (TRAM), by conducting an online survey (n = 205). Results indicate that respondents who score high on innovativeness have a higher intention to use data standards. However, we conclude that personality characteristics as described in the TRAM-model are not significant predictors of the perceived usefulness and perceived ease of use of data standards. Therefore, we suggest exploring the effects of network governance and organisational impediments to speed-up the adoption of open standards and raise interoperability in complex ecosystems.

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