Linked Open Data (LOD) comprises an unprecedented volume of structured data on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowdsourced or extracted data of often relatively low quality. We present a methodology for test-driven quality assessment of Linked Data, which is inspired by test-driven software development. We argue that vocabularies, ontologies and knowledge bases should be accompanied by a number of test cases, which help to ensure a basic level of quality. We present a methodology for assessing the quality of linked data resources, based on a formalization of bad smells and data quality problems. Our formalization employs SPARQL query templates, which are instantiated into concrete quality test case queries. Based on an extensive survey, we compile a comprehensive library of data quality test case patterns. We perform automatic test case instantiation based on schema constraints or semi-automatically enriched schemata and allow the user to generate specific test case instantiations that are applicable to a schema or dataset. We provide an extensive evaluation of five LOD datasets, manual test case instantiation for five schemas and automatic test case instantiations for all available schemata registered with Linked Open Vocabularies (LOV). One of the main advantages of our approach is that domain specific semantics can be encoded in the data quality test cases, thus being able to discover data quality problems beyond conventional quality heuristics

Online cultural heritage resources are widely available through digital libraries maintained by numerous organizations. In order to improve discoverability in cultural heritage, the typical approach is metadata aggregation, a method where centralized efforts such as Europeana improve the discoverability by collecting resource metadata. The redefinition of the traditional data models for cultural heritage resources into data models based on semantic technology has been a major activity of the cultural heritage community. Yet, linked data may bring new innovation opportunities for cultural heritage metadata aggregation. We present the outcomes of a case study that we conducted within the Europeana cultural heritage network. In this study, the National Library of The Netherlands contributed by providing the role of data provider, while the Dutch Digital Heritage Network contributed as an intermediary aggregator that aggregates datasets and provides them to Europeana, the central aggregator. We identified and analyzed the requirements for an aggregation solution for the linked data, guided by current aggregation practices of the Europeana network. These requirements guided the definition of a workflow that fulfils the same functional requirements as the existing one. The workflow was put into practice within this study and has led to the development of software applications for administrating datasets, crawling the web of data, harvesting linked data, data analysis and data integration. We present our analysis of the study outcomes and analyze the effort necessary, in terms of technology adoption, to establish a linked data approach, from the point of view of both data providers and aggregators. We also present the expertise requirements we identified for cultural heritage data analysts, as well as determining which supporting tools were required to be designed specifically for semantic data.

Linked Open Data (LOD) comprises of an unprecedented volume of structured data on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowd-sourced or extracted data of often relatively low quality. We present Databugger, a framework for test-driven quality assessment of Linked Data, which is inspired by test-driven software development. Databugger ensures a basic level of quality by accompanying vocabularies, ontologies and knowledge bases with a number of test cases. The formalization behind the tool employs SPARQL query templates, which are instantiated into concrete quality test queries. The test queries can be instantiated automatically based on a vocabulary or manually based on the data semantics. One of the main advantages of our approach is that domain specific semantics can be encoded in the data quality test cases, thus being able to discover data quality problems beyond conventional quality heuristics.

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