De Graeve, Laurens

Dimou, Anastasia

Heyvaert, Pieter

Mannens, Erik

Maroy, Wouter

Van de Walle, Rik

Verborgh, Ruben

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 an Interface for User-Friendly Linked Data Generation Administration   Anastasia Dimou, Pieter Heyvaert, Wouter Maroy, Laurens De Graeve, Ruben Verborgh, and ErikMannens   In: Proceedings of the 15th International Semantic Web Conference: Posters and Demos. , 2016.     To refer to or to cite this work, please use the citation to the published version:   Dimou, A., Heyvaert, P., Maroy, W., De Graeve, L., Verborgh, R., and Mannens, E. (2016). Towardsan Interface for User-Friendly Linked Data Generation Administration. Proceedings of the 15thInternational Semantic Web Conference: Posters and Demos.  Towards an Interface for User-FriendlyLinked Data Generation Administration?Anastasia Dimou, Pieter Heyvaert, Wouter Maroy, Laurens De Graeve,Ruben Verborgh, Erik Mannens, and Rik Van de WalleGhent University - iMinds, Belgium {firstname.lastname}@ugent.beAbstract. Linked Data generation and publication remain challengingand complicated, in particular for data owners who are not SemanticWeb experts or tech-savvy. The situation deteriorates when data frommultiple heterogeneous sources, accessed via different interfaces, is inte-grated, and the Linked Data generation is a long-lasting activity repeatedperiodically, often adjusted and incrementally enriched with new data.Therefore, we propose the rmlworkbench, a graphical user interface tosupport data owners administrating their Linked Data generation andpublication workflow. The rmlworkbench’s underlying language is rml,since it allows to declaratively describe the complete Linked Data gen-eration workflow. Thus, any Linked Data generation workflow specifiedby a user can be exported and reused by other tools interpreting rml.Keywords: Linked Data Generation, Linked Data Workbench, [R2]RML1 IntroductionAdministrating the integration of the ever-increasing amounts of data from mul-tiple sources in different formats into a common knowledge domain remainschallenging and complicated, in particular for data owners who are not Seman-tic Web experts or tech-savvy [4]. Generating Linked Data requires dealing withdata that can originally (i) reside on diverse, distributed locations, (ii) be ap-proached using different access interfaces, and (iii) be expressed in heterogeneousstructures and formats [3]. As the Linked Data generation becomes a long-lastingactivity, which is repeated periodically and is incrementally adjusted with newdata, administrating the different components becomes difficult.To minimize the effort and knowledge that data owners need to administratetheir data and the overall Linked Data generation and publication workflow,we developed a multi-user browser application, the rmlworkbench. This demoshows how data owners can use the rmlworkbench. Depending on their assignedroles, data owners can view and manage different sources for retrieving raw dataand the corresponding mappings to generate Linked Data, in contrast to ourearlier work on the rmleditor [4] which only focuses on editing mapping rules. Ascreencast of the rmlworkbench, is available at https://youtu.be/8UkI01nQNxc.?This paper’s research activities described in this paper were funded by Ghent University, iMinds,the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), theFund for Scientific Research-Flanders (FWO-Flanders) and the European Union.2 Anastasia Dimou et al.2 State of the ArtThe FluidOps Information Workbench1, Ultrwrap2 and LinDa workbench3 aregui tools supporting data owners to generate Linked Data. However, the lattertwo only support tabular data, while the former, even though it supports moredata sources, does not allow specifying different access interfaces. Most impor-tantly though, none of them allows users to export their specified Linked Datageneration workflow in a declarative, complete and interoperable way that allowsto replicate the same Linked Data generation by other tools.Linked Pipes, and its predecessor Unified Views4, are general-purpose toolsthat allow users to administrate, execute, debug, monitor and share Linked Dataprocessing tasks, for smooth and efficient management. However, they are notfocused on Linked Data generation. They perform direct mappings which areafterwards processed via sparql construct queries. Moreover, they only allow toexport the different processes descriptions, using their own custom descriptions.The Silk Workbench5 follows a similar approach as the rmlworkbench. Eventhough it is a gui supporting users to administrate rdf dataset linking, it also re-quires corresponding aspects to be specified. Its function relies on projects whichconsist of linkage rules associated with data sources (data dumps or sparqlendpoints) constituting altogether linkage tasks, as mapping rules are associatedwith data sources constituting generation tasks in the case of the rmlworkbench.3 The RML Workbench InterfaceThe rmlworkbench design principles are generic, following the classical multi-tier client-server architecture. Its underlying language to declaratively define theLinked Data generation workflow specified by the user is rml. rml [2] is a gener-alization of the wc recommended rrml mapping language [1], which is definedto specify rules to generate Linked Data from data in relational databases. rmlextends rrml to also specify rules from data in any semi-structured format,e.g., csv, xml, or json. rml was furthermore aligned with different vocabu-laries, e.g., dcat6, csvw7, or Hydra8, to specify how to access data used togenerate the desired Linked Data. The rmlworkbench considers rml as its un-derlying language, since it is the only one able to declaratively describe thecomplete Linked Data generation workflow, independently of data sources andformats [3]. Thus, all mapping rules, including the aligned data sources descrip-tion may be exported and re-used by other tools beyond the rmlworkbench toreplicate the generation of same Linked Data.1https://www.fluidops.com/en/portfolio/information_workbench/2https://capsenta.com/3https://github.com/LinDA-tools/LindaWorkbench4http://unifiedviews.eu/5https://github.com/silk-framework/silk/blob/master/doc/Workbench.md6https://www.w3.org/TR/vocab-dcat/7https://www.w3.org/TR/tabular-metadata/8https://www.hydra-cg.com/spec/latest/core/RML Workbench 3The rmlworkbench consists of five panels: Access, Retrieve, Generate, Publishand Schedule. In the remaining of this section, each panel is briefly presented.Access panel. Users can manage their own sources, which can be accessed throughinterfaces for local files, databases or Web sources. The descriptions are anno-tated using different vocabularies, e.g., dcat, csvw or Hydra. For instance, auser specifies a database accessed via a certain jdbc, and labels it “DB Source”and a dataset published on a dcat catalog, which he labels “Catalog Source”.Retrieve panel. Not all data which appear in a data source are required to gen-erate the desired Linked Data. Distinct subsets may be considered separately forgenerating different Linked Data sets. The Retrieve panel allows users to specifywhich exact data is retrieved for each selection. For instance, via the Retrievepanel, the aforementioned user specifies the exact tables, which are eventuallyconsidered to generate certain Linked Data. The user specifies the “Singers”and “Albums” tables of the “DB source” and labels them as “Singer data”and “Album data” respectively. Moreover, the same user specifies and labels as“Performance data”, among the different datasets of the “Catalog source”, thedataset about performances, and precisely its xml distribution.Generate panel. To generate the desired Linked Data, the users need to specifysets of mapping rules. The rmlworkbench allows users to (i) upload a mappingdocument, (ii) specify a Web source with mapping rules, or (iii) directly editthem via its interface. Different sets of mapping rules may be associated with thesame data, generating thus different Linked Data views. Once the set of mappingrules is associated with some raw data, the users can execute the mapping andgenerate the desired Linked Data (“Execute” button, as shown in the followingfigure). The dataset is then listed among the datasets available for publishing, orthe users are notified if the generation was not successful. The users can specifymapping rules, for instance the sets of “Singer mappings” and “Performancemappings”. Once the mapping rules are listed among the available sets, the userscan associate them with the corresponding data (“Add Logical Source” button),in our example the “Singer data” and “Performance data”. Furthermore, theusers may desire to generate another Linked Data set with the same data. Inthat case, another set of mapping rules is added, e.g., the “Person mappings”,and the user associates it with the “Singer data” as well.4 Anastasia Dimou et al.Publish panel. A frequent activity after generating Linked Data is its publica-tion. The rmlworkbench supports users to easily accomplish this activity. In ourexample, the Linked Data is published via an ldf server9. Nevertheless, the ad-ministrator can easily configure other interfaces, for instance a sparql endpoint.The users can then choose one or more of them to publish their Linked Data.Schedule panel. In most cases, the Linked Data generation and publication is arecurring activity. Data owners periodically regenerate their Linked Data set tokeep it up-to-date with the original data. The rmlworkbench allows data ownersto schedule the Linked Data generation and publication activities.To summarize, the rmlworkbench allows data owners to specify their com-plete Linked Data generation workflow, without being restricted by the tool. Inthe future, the rmleditor [4] will be integrated with the rmlworkbench and userscan then directly use the rmleditor to edit their mapping rules.References1. S. Das, S. Sundara, and R. Cyganiak. R2RML: RDB to RDF Mapping Language.Working Group Recommendation, W3C, Sept. 2012. http://www.w3.org/TR/r2rml/ .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. A. Dimou, R. Verborgh, M. Vander Sande, E. Mannens, and R. Van de Walle.Machine-Interpretable Dataset and Service Descriptions for Heterogeneous DataAccess and Retrieval. In SEMANTiCS 2015, 2015.4. P. Heyvaert, A. Dimou, A.-L. Herregodts, V. Ruben, S. Dimitri, M. Erik, andV. de Walle Rik. RMLEditor: A Graph-Based Mapping Editor for Linked DataMappings. In The Semantic Web: ESWC 2016. Springer, 2016.9https://github.com/LinkedDataFragments/Server.js

Towards an interface for user-friendly linked data generation administration

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Towards an interface for user-friendly linked data generation administration

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