Provision and consumption of linked open data: a define–produce–invoke approach


Open data is becoming increasingly popular in a wide range of service domains; however, most open datasets in Taiwan remain separate. The lack of linked open data (LOD) makes it difficult to locate and combine open datasets for the creation of innovative applications. In this study, we sought to facilitate the spread of open data in Taiwan using a novel approach referred to as define–produce–invoke (DPI). The proposed scheme employs a newly defined data query language, called LODQL (LOD query language), to allow the definition of rules for the generation of LOD by data experts. We also developed an LOD engine, which is able to produce linked open datasets and allow application developers to access LOD by invoking RESTful services. This scheme also allows data visualizations indicating the relevance of open datasets and the associations among open data items. Experiments demonstrate the feasibility and effectiveness of the proposed DPI approach.

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  1. 1.

  2. 2.


  1. 1.

    Bauer F, Kaltenböck M (2011) Linked open data: the essentials. Edition Mono/Monochrom, Vienna

    Google Scholar 

  2. 2.

    Yu L (ed) (2011) Linked open data. In: A developer’s guide to the semantic web. Springer, Berlin, pp 409–466

  3. 3.

    Janssen M, Charalabidis Y, Zuiderwijk A (2012) Benefits, adoption barriers and myths of open data and open government. Inf Syst Manag 29:258–268

    Article  Google Scholar 

  4. 4.

    Bizer C, Heath T, Berners-Lee T (2009) Linked data-the story so far. Int J Semant Web Inf Syst 5(3):1–22

    Article  Google Scholar 

  5. 5.

    Prud E, Seaborne A (2006) SPARQL query language for RDF

  6. 6.

    Meroño-Peñuela A, Ashkpour A, Guéret C, Schlobach S (2017) CEDAR: the Dutch historical censuses as linked open data. Semant Web 8:297–310

    Article  Google Scholar 

  7. 7.

    Jett J, Cole TW, Han M-JK, Szylowicz C (2017) Linked open data (LOD) for library special collections. Presented at the proceedings of the 17th ACM/IEEE joint conference on digital libraries, Toronto

  8. 8.

    Sheridan J, Tennison J (2010) Linking UK Government Data. In: Ldow

  9. 9.

    Tilkov S (2007) A brief introduction to REST. In: InfoQ, vol 10

  10. 10.

    Peng Y-Y, Ma S-P, Lee J (2009) REST2SOAP: a framework to integrate SOAP services and RESTful services. In: 2009 IEEE international conference on service-oriented computing and applications (SOCA), pp 1–4

  11. 11.

    McBride B (2002) Jena: a semantic web toolkit. IEEE Internet Comput 6:55–59

    Article  Google Scholar 

  12. 12.

    Zhu NQ (2013) Data visualization with D3. js cookbook. Packt Publishing Ltd, Birmingham

    Google Scholar 

  13. 13.

    Klímek J, Škoda P, Necaský M (2016) Requirements on linked data consumption platform. In: WWW 2016 workshop: linked data on the web (LDOW2016)

  14. 14.

    Joshi AK, Jain P, Hitzler P, Yeh PZ, Verma K, Sheth AP et al (2012) Alignment-based querying of linked open data. Springer, Berlin, pp 807–824

    Google Scholar 

  15. 15.

    AlObaidi M, Mahmood K, Sabra S (2016) Semantic enrichment for local search engine using linked open data. In: Proceedings of the 25th international conference companion on world wide web, pp 631–634

  16. 16.

    Norton B, Krummenacher R (2010) Consuming dynamic linked data. In: Proceedings of the 1st international conference on consuming linked data, vol 665, pp 25–36

  17. 17.

    Nowack B (2009) Paggr: linked data widgets and dashboards. Web Semant Sci Serv Agents World Wide Web 7:272–277

    Article  Google Scholar 

  18. 18.

    Mouzakitis S, Attard J, Danitz R, Farid L, Fotopoulou E, Galkin M et al (2015) LinDA—linked data for SMEs.

  19. 19.

    Bizer C, Seaborne A (2004) D2RQ-treating non-RDF databases as virtual RDF graphs. In: Proceedings of the 3rd international semantic web conference (ISWC2004)

  20. 20.

    (2018) utilization support system.

  21. 21.

    Bechhofer S (2009) OWL: web ontology language. In: Liu L, Özsu MT (eds) Encyclopedia of database systems. Springer, Berlin, pp 2008–2009

    Google Scholar 

  22. 22.

    Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507

    MathSciNet  Article  Google Scholar 

  23. 23.

    Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems 27 (NIPS 2014)

Download references


This research was sponsored by Ministry of Science and Technology in Taiwan under the Grant MOST 105-2221-E-019-054-MY3. Special thanks to Mr. Chih Chun Huang for his valuable feedback to our research.

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Correspondence to Shang-Pin Ma.

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Ma, SP., Chen, PZ., Lee, WT. et al. Provision and consumption of linked open data: a define–produce–invoke approach. SOCA 12, 211–226 (2018).

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  • Linked data
  • Open data
  • Linked open data
  • Service generation
  • Data consumption
  • Data visualization