Dimou, Anastasia

Heyvaert, Pieter

Mannens, Erik

Van de Walle, Rik

Verborgh, Ruben

English

Ghent University Academic Bibliography

  biblio.ugent.be   The UGent Institutional Repository is the electronic archiving and dissemination platform for allUGent research publications. Ghent University has implemented a mandate stipulating that allacademic publications of UGent researchers should be deposited and archived in this repository.Except for items where current copyright restrictions apply, these papers are available in OpenAccess.   This item is the archived peer-reviewed author-version of:   Towards Approaches for Generating RDF Mapping Definitions   Pieter Heyvaert, Anastasia Dimou, Ruben Verborgh, Erik Mannens, and Rik Van de Walle   In: Proceedings of the ISWC 2015 Posters & Demonstrations Track, 2015.     To refer to or to cite this work, please use the citation to the published version:   Heyvaert, P., Dimou, A., Verborgh, R., Mannens, E., and Van de Walle, R. (2015). TowardsApproaches for Generating RDF Mapping Definitions. Proceedings of the ISWC 2015 Posters &Demonstrations Track Towards Approaches for Generating RDFMapping DefinitionsPieter Heyvaert, Anastasia Dimou,Ruben Verborgh, Erik Mannens, and Rik Van de WalleGhent University - iMinds - Multimedia Lab,pheyvaer.heyvaert@ugent.beAbstract. Obtaining Linked Data by modeling domain-level knowledgederived from input data is not straightforward for data publishers, espe-cially if they are not Semantic Web experts. Developing user interfacesthat support domain experts to semantically annotate their data becamefeasible, as the mapping rules were abstracted from their execution. How-ever, most existing approaches reflect how mappings are typically exe-cuted: they offer a single linear workflow, triggered by a particular datasource. Alternative approaches were neither thoroughly investigated yet,nor incorporated in most existing user interfaces for mappings. In thispaper, we generalize the two prevalent approaches for generating map-pings of data in databases: database-driven and ontology-driven, to beapplicable for any other data structure; and introduce two approaches:model-driven and result-driven.1 IntroductionA substantial amount of Linked Data is generated from data that exists in het-erogeneous formats and comes from different sources. This generation processis facilitated by mapping languages, such as the wc recommended rrml [1]or its extended version rml [2], which separate the definition of mappings fromtheir execution. While data publishers are domain experts—the intended cre-ators of mappings—manually creating and editing mapping definitions requiresknowledge of the mapping language’s syntax, which is unpractical for most pub-lishers [3]. Therefore, user interfaces can facilitate domain experts to specifymappings much more conveniently.Pinkel et al. [3] introduced two types of approaches for editing mappingsfor data in relational databases to the Resource Description Framework (rdf),namely (i) the database-driven and (ii) the ontology-driven, both of which areimplemented at fluidOps1. The suitability of each approach depends on differ-ent factors. However, most existing mapping interfaces, which mainly refer todata in databases, support only one of the two approaches. By doing so, datapublishers’ editing options are restricted. Alternative approaches beside the two1 http://www.fluidops.com/2 Pieter Heyvaert et al.id     name     age1      John       252      Jane       24<http://www.example.com/1>   a     foaf:Person<http://www.example.com/1> foaf:name “John”<http://www.example.com/2>   a     foaf:Person<http://www.example.com/2> foaf:name “Jane”foaf:Personfoaf:Documentfoaf:Agentdcterms:Agentfoaf:namefoaf:genderfoaf:Agedcterms:titleclasses properties(a) data-drivenid     name     age1      John       252      Jane       24<http://www.example.com/1>   a     foaf:Person<http://www.example.com/1> foaf:name “John”<http://www.example.com/2>   a     foaf:Person<http://www.example.com/2> foaf:name “Jane”foaf:Personfoaf:Documentfoaf:Agentdcterms:Agentfoaf:namefoaf:genderfoaf:Agedcterms:titleclasses properties(b) schema-drivenid     name     age1      John       252      Jane       24<http://www.example.com/1>   a     foaf:Person<http://www.example.com/1> foaf:name “John”<http://www.example.com/2>   a     foaf:Person<http://www.example.com/2> foaf:name “Jane”foaf:Personfoaf:Documentfoaf:Agentdcterms:Agentfoaf:namefoaf:genderfoaf:Agedcterms:titleclasses propertiesnameperson(c) model-drivenid     name     age1      John       252      Jane       24<http://www.example.com/1>   a     foaf:Person<http://www.example.com/1> foaf:name “John”<http://www.example.com/2>   a     foaf:Person<http://www.example.com/2> foaf:name “Jane”foaf:Personfoaf:Documentfoaf:Agentdcterms:Agentfoaf:namefoaf:genderfoaf:Agedcterms:titleclasses properties(d) result-drivenFig. 1: Conceptual comparison of approaches to generate mappings. Input data,vocabularies, mappings and rdf triples are involved in different combinations.aforementioned ones were not thoroughly investigated so far, even though theymight be more adequate under different circumstances.Moreover, the identified approaches are limited to modeling data in databases.Thus, the implementations completely disregard heterogeneous formats. Addi-tionally, these implementations fail to take into account and combine multi-ple data sources [4]. In this paper, we therefore generalize the approaches todata-driven and schema-driven and introduce two alternatives based on obser-vation: the model-driven and the result-driven approaches.2 ApproachesOur goal is to introduce mapping generation approaches that cover more thor-oughly the different needs and alternative usage scenarios: ranging from seman-tically annotating a particular data source to modeling domain-level knowledge.Each of the approaches is described in detail below and visualized in Figure 1.Data-Driven Mapping Definitions GenerationIn the database-driven approach [3], data publishers have the data from thedatabase available. The generation of the mappings is based on that data, namelydata fractions are iteratively associated to a corresponding mapping rule. ForTowards Approaches for Generating RDF Mapping Definitions 3instance, the fluidOps and Karma2 interfaces follow this approach. However,from a more general point of view, this approach could be applicable to anytype of input data, and any combination of them, besides relational databases.Thus, we introduce a more generic approach, so-called data-driven (Figure 1a).Instead of only considering data from a database, any number of input datasources in any format, such as csv, xml, json, is equally considered.Schema-Driven Mapping Definitions GenerationAn existing ontology can be used as the basis for generating the mappings. Thisis the ontology-driven approach [3], which is supported by the fluidOps editor.Hence, generating the mappings is driven in the first place by the schema, asit accrues from the ontology. Afterwards, data publishers edit the mappings byassociating them to the applicable data from the input source(s). In contrast tothe data-driven approach where the correct schema(s) is associated to the data,here the appropriate data is associated to the schema(s). While Pinkel et al. [3]consider a single ontology, the approach can be more generic and applied to anyschema, namely any combination of ontologies and/or vocabularies. Thus, weconsider the more generic notion of schema-driven approach (Figure 1b).Model-Driven Mapping Definitions GenerationAlternatively, data publishers can firstly model the domain, by generating ab-stract mappings. More precisely, data publishers define the entities, their at-tributes and their relationships to other entities, without explicitly indicatingneither the schema (ontologies and vocabularies) nor the input data fractions tobe used. The model is subsequently instantiated by applying adequate schema(s)and it is associated with input data, by specifying which fractions of the inputdata sources are associated with which parts of the model. To this end, weintroduce the model-driven approach (Figure 1c). While this approach is prac-tical and useful, for instance applied by De Vocht et al. [5], to the best of ourknowledge, no user interface supports it. However, it enables data publishers toformally generate abstract definitions and instantiate them afterwards with theappropriate data and schema(s).Result-Driven Mapping Definitions GenerationLast, we introduce the result-driven approach (Figure 1d), where mappings canbe generated based on the desired results. To be more precise, from a desired rdfoutput, the mappings are generated based on the desired output’s model andschema(s). Afterwards, they are associated with data fractions from the inputsources. In contrast to the data-driven approach, which is based on the inputdata and where the appropriate schema is subsequently chosen, this approach isbased on the desired result and the model, together with the proper schema(s),is derived from it. A real-world example of this approach is transformy.io3.2 http://usc-isi-i2.github.io/karma/3 https://www.transformy.io4 Pieter Heyvaert et al.3 DiscussionThis paper lists approaches to generate mappings. Besides the ones mentioned,hybrid approaches might emerge when implementing them or as data publishersspecify their mappings. Identifying the different approaches, together with theiradvantages, allows publishers to select the approach best suited for the task athand. Starting with a particular approach does not necessarily mean that datapublishers can/should not switch between approaches over the course of a map-pings’ editing time. Thus, a user interface should allow and support switchingbetween multiple approaches as suggested by Pinkel et al. [3]. The user interfaceof our prototype mapping editor, the rmleditor4, aims to validate the aforemen-tioned approaches by creating the conditions for data publishers to follow anyof them. This is facilitated by simultaneously offering three different panels todata publishers: (i) Input Panel (i.e., the input data), (ii) Modeling Panel (i.e.,the mappings); and (iii) Results Panel (i.e., the output rdf dataset).Acknowledgements. The described research activities were funded by GhentUniversity, iMinds, the Institute for the Promotion of Innovation by Science andTechnology in Flanders (IWT), the Fund for Scientific Research Flanders (FWOFlanders), and the European Union.References[1] Souripriya Das, Seema Sundara, and Richard Cyganiak. R2RML: RDB toRDF Mapping Language. Working group recommendation, W3C, September2012. URL http://www.w3.org/TR/r2rml/.[2] Anastasia Dimou, Miel Vander Sande, Pieter Colpaert, Ruben Verborgh, ErikMannens, and Rik Van de Walle. RML: A Generic Language for IntegratedRDF Mappings of Heterogeneous Data. In Workshop on Linked Data on theWeb, 2014.[3] Christoph Pinkel, Carsten Binnig, Peter Haase, Clemens Martin, Kunal Sen-gupta, and Johannes Trame. How to best find a partner? An evaluation ofediting approaches to construct R2RML mappings. In The Semantic Web:Trends and Challenges, pages 675–690. Springer, 2014.[4] Christoph Pinkel, Carsten Binnig, Ernesto Jime´nez-Ruiz, Wolfgang May, Do-minique Ritze, Martin G Skjæveland, Alessandro Solimando, and EvgenyKharlamov. RODI: A Benchmark for Automatic Mapping Generation inRelational-to-Ontology Data Integration. In The Semantic Web. Latest Ad-vances and New Domains, pages 21–37. Springer, 2015.[5] Laurens De Vocht, Mathias Van Compernolle, Anastasia Dimou, Pieter Col-paert, Ruben Verborgh, Erik Mannens, Peter Mechant, and Rik Van deWalle. Converging on semantics to ensure local government data reuse. Pro-ceedings of the 5th workshop on Semantics for Smarter Cities (SSC14), 13thInternational Semantic Web Conference (ISWC), 2014.4 http://rml.io/RML_editor.html

Towards approaches for generating RDF mapping definitions

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Towards approaches for generating RDF mapping definitions

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