Test-driven evaluation of linked data quality

Abstract

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

Challenges for Value-driven Semantic Data Quality Management

“…Semantic data quality tools from the state of the art such as TCD's Dacura Quality Service for OWLbased validation (Feeney, 2017) and AKSW's RDFUnit tool for SPARQL and SHACL-based data unit testing (Kontokostas, 2014).…”

“…Automated support for data stakeholder or steward governance of data quality is immature due to the lack of standardisation of integration points for big data quality control systems (ISO, 2014). In contrast, for low level data quality metrics, recent advances in semantic data quality analysis show great promise (Bertossi, 2013, Feeney, 2016, Kontokostas, 2014 but methods have not yet emerged to apply them to traditional databases and semi-structured web data, where most data growth is centred. Current work on dataset meta-data standards by the W3C (Maali, 2014) would be a natural basis for business-oriented quality metadata.…”

Enriching and improving the quality of linked data with GIS

“…DBpedia content is automatically generated from Wikipedia with limited supervision and is prone to errors. According to the research conducted by Kontokostas et al (2014) [20], 28K resources in the English language version of DBpedia share the same coordinates with another resource. This type of location error often results from the relatively low accuracy, with which coordinates are expressed and the degree of location generalization.…”

Sustainable Linked Data Generation: The Case of DBpedia

“…We distinguish two types of issues with dbpedia: schema-level rules that define how to apply vocabularies to raw data [6,10,14], e.g., the dbo:militaryBranch property is used for entities of dbo:MilitaryUnit type, but it should only be used with entities of dbo:Person type [13]. data-level extracted, or processed and transformed data values.…”