RDFUnit: an RDF Unit-Testing suite

RDFUnit is a test driven data-debugging framework that can run automatically generated (based on a schema) and manually generated test cases against an endpoint. All test cases are executed as SPARQL queries using a pattern-based transformation approach.

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For more information on our methodology please refer to our report:

Test-driven evaluation of linked data quality. Dimitris Kontokostas, Patrick Westphal, Sören Auer, Sebastian Hellmann, Jens Lehmann, Roland Cornelissen, and Amrapali J. Zaveri in Proceedings of the 23rd International Conference on World Wide Web.

RDFUnit in a Nutshell

  • Test case: a data constraint that involves one or more triples. We use SPARQL as a test definition language.
  • Test suite: a set of test cases for testing a dataset
  • Status: Success, Fail, Timeout (complexity) or Error (e.g. network). A Fail can be an actual error, a warning or a notice
  • Data Quality Test Pattern (DQTP): Abstract test cases that can be intantiated into concrete test cases using pattern bindings
  • Pattern Bindings: valid replacements for a DQTP variable
  • Test Auto Generators (TAGs): Converts RDFS/OWL axioms into concrete test cases

As shown in the figure, there are two major sources for creating test cases. One source is stakeholder feedback from everyone involved in the usage of a dataset and the other source is the already existing RDFS/OWL schema of a dataset. Based on this, there are several ways in which test cases can be created:

  • Using RDFS/OWL constraints directly: Test cases can be automatically created via TAGs in this case.
  • Enriching the RDFS/OWL constraints: Since many datasets provide only limited schema information, we perform automatic schema enrichment. These schema enrichment methods can take an RDF/OWL dataset or a SPARQL endpoint as input and automatically suggest schema axioms with a certain confidence value by analysing the dataset. In our methodology, this is used to create further test cases via TAGs. It should be noted that test cases are explicitly labelled, such that the engineer knows that they are less reliable than manual test cases.
  • Re-using tests based on common vocabularies: Naturally, a major goal in the Semantic Web is to re-use existing vocabularies instead of creating them from scratch for each dataset. We detect the used vocabularies in a dataset, which allows to re-use test cases from a test case pattern library.
  • Instantiate existing DQTPs: The aim of DQTPs is to be generic, such that they can be applied to different datasets. While this requires a high initial effort of compiling a pattern library, it is beneficial in the long run, since they can be re-used. Instead of writing SPARQL templates themselves, an engineer can select and instantiate the correct DQTP. This does not necessarily require SPARQL knowledge, but can also be achieved via a textual description of a DQTP, examples and its intended usage.
  • Write own DQTPs: In some cases, test cases cannot be generated by any of the automatic and semi-automatic methods above and have to be written from scratch by an engineer. These DQTPs can then become part of a central library to facilitate later re-use.

Publications

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News

DBpedia @ SEMANTiCS 2017 ( 2017-09-04T15:25:14+02:00 by Sandra Bartsch)

2017-09-04T15:25:14+02:00 by Sandra Bartsch

We are happy to invite you to the 10th DBpedia Community Meeting which will be held in Amsterdam. During the SEMANTiCS 2017, Sep 11-14, the DBpedia Community will get together on the 14th of September for the DBpdia Day. Read more about "DBpedia @ SEMANTiCS 2017"

PRESS RELEASE: Amsterdam​ ​-​ ​this​ ​year’s​ ​hotspot​ ​​on Linked​ ​Data​ ​Strategies​ ​&​ ​Practices ( 2017-09-04T11:58:06+02:00 by Sandra Bartsch)

2017-09-04T11:58:06+02:00 by Sandra Bartsch

September 11-14, 2017 international experts from science and industry demonstrate the business value of smart data services at SEMANTiCS 2017 Experts from science and industry meet at Europe’s biggest Linked Data and Semantic Web event to present and discuss latest … Continue reading → Read more about "PRESS RELEASE: Amsterdam​ ​-​ ​this​ ​year’s​ ​hotspot​ ​​on Linked​ ​Data​ ​Strategies​ ​&​ ​Practices"

AKSW Colloquium, 01.09.2017, IDOL: Comprehensive & Complete LOD Insights ( 2017-08-28T17:24:03+02:00 Gustavo Publio)

2017-08-28T17:24:03+02:00 Gustavo Publio

At the AKSW Colloquium on Friday 1st of September, at 10:40 AM there will be a paper presentation by Gustavo Publio. Read more about "AKSW Colloquium, 01.09.2017, IDOL: Comprehensive & Complete LOD Insights"

AKSW at ISWC2017 ( 2017-07-30T05:57:57+02:00 Muhammad Saleem)

2017-07-30T05:57:57+02:00 Muhammad Saleem

We are very pleased to announce that AKSW will be presenting 2 papers at ISWC 2017, which will be held on 21-24 October in Vienna, Austria. The demo and workshops papers have to be announced. Read more about "AKSW at ISWC2017"

AKSW Colloquium, 07.07.2017, Two paper presentations concerning Link Discovery and Knowledge Base Reasoning ( 2017-07-06T21:24:36+02:00 by Daniel Obraczka)

2017-07-06T21:24:36+02:00 by Daniel Obraczka

At the AKSW Colloquium on Friday 7th of July, at 10:40 AM there will be two paper presentations concerning genetic algorithms to learn linkage rules, and differentiable learning of logical rules for knowledge base reasoning. Read more about "AKSW Colloquium, 07.07.2017, Two paper presentations concerning Link Discovery and Knowledge Base Reasoning"