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Linked Open Vocabulary Recommendation Based on Ranking and Linked Open Data

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9544)


The vocabulary space of the Semantic Web includes more than 500 vocabularies according to the Linked Open Vocabularies (LOV) initiative that maintains the directory list and provides search functionality on top of the curated data. Domain experts and researchers have populated it to facilitate the interpretation and exchange of information in the Web of Data. The abundance of vocabularies and terms available in the LOV space, on one hand aims to cover the major knowledge management needs, but on the other hand it could be cumbersome for a non-expert or even a vocabulary expert to find the correct way through the collection. To address this problem, we present an approach that helps to identify the most appropriate set of LOV vocabulary terms for a given Web content context by leveraging the existing dynamics within the LOV graph and the usage patterns in the LOD cloud. The paper describes the framework architecture that enables the discovery of vocabularies; it focuses on the corresponding metrics and algorithm, and discusses the outcomes of the applied experiments.


  • Natural Language Processing
  • Ranking Score
  • Semantic Annotation
  • Candidate Term
  • Outgoing Link

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-31676-5_3
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  1. Atemezing, G.A., Troncy, R.: Information content based ranking metric for linked open vocabularies. In: Proceedings of the 10th International Conference on Semantic Systems, pp. 53–56. ACM (2014)

    Google Scholar 

  2. Auer, S., Demter, J., Martin, M., Lehmann, J.: LODstats – an extensible framework for high-performance dataset analytics. In: ten Teije, A., Völker, J., Handschuh, S., Stuckenschmidt, H., d’Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS, vol. 7603, pp. 353–362. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  3. Bizer, C., Eckert, K., Meusel, R., Mühleisen, H., Schuhmacher, M., Völker, J.: Deployment of RDFa, microdata, and microformats on the web – a quantitative analysis. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 17–32. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  4. Butt, A.S.: Ontology search: finding the right ontologies on the web. In: Proceedings of the 24th International Conference on World Wide Web Companion, pp. 487–491. International World Wide Web Conferences Steering Committee (2015)

    Google Scholar 

  5. Sahar Butt, A., Haller, A., Xie, L.: Relationship-based top-k concept retrieval for ontology search. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) EKAW 2014. LNCS, vol. 8876, pp. 485–502. Springer, Heidelberg (2014)

    Google Scholar 

  6. Guha, R.: Introducing search engines come together for a richer web (2011).

  7. Heath, T., Bizer, C.: Linked data: evolving the web into a global data space. Synth. Lect. Semant. Web: Theory Technol. 1(1), 1–136 (2011)

    Google Scholar 

  8. Hogan, A., Harth, A., Passant, A., Decker, S., Polleres, A.: Weaving the Pedantic Web (2010)

    Google Scholar 

  9. Käfer, T., Harth, A.: Billion Triples Challenge data set (2014). Downloaded from

  10. Meusel, R., Paulheim, H.: Heuristics for fixing common errors in deployed microdata. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 152–168. Springer, Heidelberg (2015)

    CrossRef  Google Scholar 

  11. Meusel, R., Petrovski, P., Bizer, C.: The WebDataCommons microdata, RDFa and microformat dataset series. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 277–292. Springer, Heidelberg (2014)

    Google Scholar 

  12. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report 1999–66, Stanford InfoLab, November 1999

    Google Scholar 

  13. Schaible, J., Gottron, T., Scheglmann, S., Scherp, A.: Lover: support for modeling data using linked open vocabularies. In: Proceedings of the Joint EDBT/ICDT Workshops, pp. 89–92. ACM (2013)

    Google Scholar 

  14. Schaible, J., Gottron, T., Scherp, A.: Survey on common strategies of vocabulary reuse in linked open data modeling. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 457–472. Springer, Heidelberg (2014)

    CrossRef  Google Scholar 

  15. Stadtmüller, S., Harth, A., Grobelnik, M.: Accessing information about linked data vocabularies with vocab. cc. In: Li, J., Qi, G., Zhao, D., Nejdl, W., Zheng, H.-T. (eds.) Semantic Web and Web Science, pp. 391–396. Springer, New York (2013)

    CrossRef  Google Scholar 

  16. Stavrakantonakis, I., Fensel, A., Fensel, D.: Matching web entities with potential actions. In: SEMANTICS (2014)

    Google Scholar 

  17. Stavrakantonakis, I., Toma, I., Fensel, A., Fensel, D.: Hotel websites, web 2.0, web 3.0 and online direct marketing: the case of Austria. In: Xiang, Z., Tussyadiah, L. (eds.) Information and Communication Technologies in Tourism, pp. 665–677. Springer, Switzerland (2014)

    Google Scholar 

  18. Vandenbussche, P.-Y., Vatant, B.: Linked open vocabularies. ERCIM News 96, 21–22 (2014)

    Google Scholar 

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This work has been partially supported by the EU projects BYTE, ENTROPY, EUTravel, FWF project OntoHealth, as well as FFG projects OpenFridge and TourPack.

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Correspondence to Ioannis Stavrakantonakis .

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Stavrakantonakis, I., Fensel, A., Fensel, D. (2016). Linked Open Vocabulary Recommendation Based on Ranking and Linked Open Data. In: Qi, G., Kozaki, K., Pan, J., Yu, S. (eds) Semantic Technology. JIST 2015. Lecture Notes in Computer Science(), vol 9544. Springer, Cham.

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