Dimou, Anastasia

Freudenberg, M.

Hellmann, S.

Kontokostas, D.

Lehmann, J.

Mannens, Erik

Van de Walle, Rik

Verborgh, Ruben

Ghent University Academic Bibliography

Test-driven Assessment of [R2]RML Mappingsto Improve Dataset QualityAnastasia Dimou1, Dimitris Kontokostas2, Markus Freudenberg2,Ruben Verborgh1, Jens Lehmann2, Erik Mannens1,Sebastian Hellmann2, and Rik Van de Walle11 Ghent University - iMinds - Multimedia Lab, Belgium{firstname.lastname}@ugent.be2 Universitat Leipzig, Institut fur Informatik, AKSW, Germany{lastname}@informatik.uni-leipzig.deAbstract. rdf dataset quality assessment is currently performed pri-marily after data is published. Incorporating its results, by applying cor-responding adjustments to the dataset, happens manually and occursrarely. In the case of (semi-)structured data (e.g., csv, xml), the rootof the violations often derives from the mappings that specify how therdf dataset will be generated. Thus, we suggest shifting the quality as-sessment from the rdf dataset to the mapping definitions that generateit. The proposed test-driven approach for assessing mappings relies onrdfunit test cases applied over mappings specified with rml. Our eval-uation is applied to different cases, e.g., dbpedia, and indicates that theoverall quality of an rdf dataset is quickly and significantly improved.Keywords: Linked Data Mapping, Data Quality, rml, rrml, rdfunit1 IntroductionAlthough more and more data is published as Linked Data, there are significantvariations in quality [6], commonly conceived as “fitness for use” for a certainapplication or use case. Similar violation patterns reoccur frequently, and mostencountered violations are related to the dataset’s schema, namely the vocabu-laries or ontologies used to annotate the data [4]. When datasets stem originallyfrom semi-structured formats (e.g., csv, xml), the schema is derived from theset of classes and properties specified by the mappings which are applied re-peatedly. Consequently, the same violations are repeated in the dataset as well.Lately, combinations of different ontologies and vocabularies are used to anno-tate data [5]. This increases the likelihood of such violations, as they often derivefrom incorrect usage or incorrect combinations of schemas in the mappings.Taking mappings of data to rdf as a software engineering task, a set of unittest cases can be assigned to the mappings to ensure the correct generation ofrdf datasets from input data. Incorporating quality assessment as part of themapping is essential to prevent the same violations from appearing repeatedlywithin the dataset and over distinct entities. After all, structural adjustments2 Anastasia Dimou et al.can still be applied in this phase, as violations are identified at their root. More-over, if mappings are assessed, every other new data source also mapped usingthem directly benefits from the improvements. Therefore, we proposed a uniformsolution [1] that assesses the quality of an rdf dataset, covering both the map-pings and the dataset. In this work, we aim to elaborate more on how rdfunitpatterns [4] for dataset test cases were arose to cover rml mappings [2], too.2 [R2]RML Mappings Quality Assessment with RDFUnitOur solution relies on the rdf mapping language (rml) [2] that allows specifyingmapping definitions expressed in rdf, and the rdfunit validation frameworkdue to its associated test-case-based architecture [3]. For our proof-of-conceptimplementation3, rdfunit test cases are applied to mappings defined with rml.RML extends rrml4, the wc recommended language for defining mappingsof data in relational databases to rdf, and also covers mappings from sources indifferent semi-structured formats, such as csv and json [2]. rml documents [2]specify how the input data can be represented in rdf. The main building blocksof rml documents are Triples Maps that define how triples are generated andconsist of three main parts: the Logical Source, the Subject Map and zero or morePredicate-Object Maps. Term Maps define how rdf terms (iri, blank node or literal)are generated. A Term Map can be constant-valued that always generates the samerdf term, reference-valued that is the data value of a referenced data fragmentin a given Logical Source, or template-valued which is a valid string template thatcan contain referenced data fragments of a given Logical Source.RDFUnit [4] is an rdf validation framework inspired by test-driven softwaredevelopment. In rdfunit, every vocabulary, ontology, dataset or application canbe associated by a set of data quality test cases. The test case definition languageof rdfunit is sparql, convenient to directly query for identifying violations. Forrapid test case instantiation, a pattern-based sparql-template engine, runningover a library of common patterns5, is supported where variables can be easilybound into patterns. rdfunit has a Test Auto Generator (tag) component. tagsearches for schema information and automatically instantiates new test cases.As [r]rml mappings can be processed as rdf documents, because of theirnative rdf representation and viewpoint (written as the generated triples), thesame set of schema validation patterns normally applied on the rdf dataset isalso applicable on the mappings that state how it is generated. Nevertheless,instead of validating the triple’s predicate against its subject and object, thepredicate is extracted from the Predicate Map and is validated against the TermMaps that generate the subject and object. To achieve this, the properties andclasses are identified and their namespaces are used to retrieve the schemas and3https://github.com/mmlab/RMLValidator4http://www.w3c.org/TR/R2RML5https://github.com/AKSW/RDFUnit/blob/master/configuration/patterns.ttlTest-driven Assessment of [R2]RML Mappings 3Fig. 1. (i) rml mapping (left) (ii) and corresponding example of a generated triple (right).The range (xsd:integer) of the specified predicate (foaf:age) is different compared to theone in the Object Map (xsd:float) causing a violation. Similarly occurs for the Subject Map.generate the test cases as if they were the actual dataset. The expected value,as derived from the Predicate Map, is compared to the defined one, as derivedfrom the corresponding Subject Map and Object Map. For example, the extractedpredicate is foaf:age normally expects an instance of foaf:Agent type for itsdomain and an integer datatype for its range, but the Term Map that generatesthe subject is defined to be of foaf:Project type and the object is defined tohave a float value. Its mapping document follows:<#Mapping> rr:subjectMap [rr:template "http://example.com/{id}"; rr:class foaf:Project];rr:predicateObjectMap [rr:predicate foaf:age; rr:objectMap [rml:reference "age"]].Corresponding rdfunit test cases and patterns were defined to apply tothe mappings, adjusting the assessment queries.6 The defined test cases coverall possible alternative ways of defining equivalent mappings that generate thesame triples. rdfunit can annotate test cases by requesting additional variablesand binding them to specific result properties. The test case patterns applied tothe aforementioned example and its instantiation are indicatively presented. Onthe left, the where clause of a sparql template query that assesses the datatypeis presented. On the right it is presented how it is instantiated:?resource %%P1%% ?c.FILTER (DATATYPE(?c) != %%D1%%)?resource foaf:age ?c.FILTER (DATATYPE(?c) != xsd:int)The following is the where clause of the same test case applied to the mapping:?resource rr:predicateObjectMap ?poMap.?poMap rr:predicate %%P1%%;rr:objectMap ?objM.?objM rr:datatype ?c.FILTER (?c != %%D1%%)?resource rr:predicateObjectMap ?poMap.?poMap rr:predicate foaf:age;rr:objectMap ?objM.?objM rr:datatype ?c.FILTER (?c != xsd:int)3 Evaluation and DiscussionThe assessed datasets and corresponding mappings, as well as the assessmentresults are summarized in Table 1: dbpedia mappings7, after the mappings wereconverted from wikitext markup to rml8, and its dataset were assessed. dblp6https://github.com/AKSW/RDFUnit/blob/master/data/tests/Manual/www.w3.org/ns/r2rml/rr.tests.Manual.ttl7http://mappings.dbpedia.org/8https://goo.gl/GPB1Ar4 Anastasia Dimou et al.dataset assessment mapping assessmentdataset size time #fail. #viol. size time #fail. #viol. triplesDBpEn 62M 16h 1,128 3.2M 115K 11s 1 160 255KDBpNL 21M 1.5h 683 815K 53K 6s 1 124 106KDBLP 12M 12h 7 8.1M 368 12s 2 8 8MiLastic10 150K 12s 23 37K 825 15s 3 26 37KCDFLG11 0.6K 7s 15 678 558 13s 4 16 631CEUR-WS12 2.4K 6s 7 783 702 5s 3 12 783Table 1. The number of triples (size), number of test cases, evaluation time, failed testcases and total individual violations appear for both dataset and mapping assessment.mappings, after the mappings were converted to rml9, and the correspondingdataset were assessed, too. The results show that the required quality assessmenttime is significantly reduced if the mappings are assessed instead of the rdfdataset, especially in the case of medium/large datasets. That happens becausethe dataset assessment requires examining each triple separately to identify,for instance, that 12M triples violated the predicate’s range, whereas mappingassessment requires only 1 triple to be examined. The effectiveness of mappingassessments is also high: the identified violations can be accurately indicated.Acknowledgements. This paper’s research activities were funded by GhentUniversity, iMinds, the Institute for the Promotion of Innovation by Science andTechnology in Flanders, the Fund for Scientific Research-Flanders and by grantsfrom the EU’s 7th & H2020 Programmes for projects ALIGNED (GA 644055),GeoKnow (GA 318159) and LIDER (GA 610782).References1. A. Dimou, D. Kontokostas, M. Freudenberg, R. Verborgh, J. Lehmann, E. Mannens,S. Hellmann, and R. Van de Walle. Assessing and Refining Mappings to RDF toImprove Dataset Quality. In Proceedings of the 14th International Semantic WebConference, Oct. 2015.2. A. Dimou, M. Vander Sande, P. Colpaert, R. Verborgh, E. Mannens, and R. Van deWalle. RML: A Generic Language for Integrated RDF Mappings of HeterogeneousData. In Workshop on Linked Data on the Web, 2014.3. D. Kontokostas, M. Bru¨mmer, S. Hellmann, J. Lehmann, and L. Ioannidis. NLPdata cleansing based on Linguistic Ontology constraints. In Proc. of the ExtendedSemantic Web Conference 2014, 2014.4. D. Kontokostas, P. Westphal, S. Auer, S. Hellmann, J. Lehmann, R. Cornelissen,and A. Zaveri. Test-driven Evaluation of Linked Data Quality. In Proceedings ofthe 23rd International Conference on World Wide Web, pages 747–758, 2014.5. M. Schmachtenberg, C. Bizer, and H. Paulheim. Adoption of the Linked Data BestPractices in Different Topical Domains. volume 8796 of LNCS. Springer, 2014.6. A. Zaveri, A. Rula, A. Maurino, R. Pietrobon, J. Lehmann, and S. Auer. QualityAssessment for Linked Data: A Survey. Semantic Web Journal, 2015.9https://github.com/RMLio/D2RQ_to_R2RML.git10http://explore.ilastic.be/11http://ewi.mmlab.be/cd/all12http://rml.io/rml/data/SPC2015/

Test-driven assessment of [r2]rml mappings to improve dataset quality

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Test-driven assessment of [r2]rml mappings to improve dataset quality

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