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Joint International Semantic Technology Conference

JIST 2015: Semantic Technology pp 40–55Cite as

Linked Open Vocabulary Recommendation Based on Ranking and Linked Open Data

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

Abstract

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.

Keywords

  • 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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Fig. 1.
Fig. 2.

Notes

  1. 1.

    http://www.webdatacommons.org/structureddata/.

  2. 2.

    E.g. the schema.org profile is available under the URL: http://lov.okfn.org/dataset/lov/vocabs/schema.

  3. 3.

    http://swoogle.umbc.edu/.

  4. 4.

    http://lov.okfn.org/dataset/lov/sparql.

  5. 5.

    By notifying the server that the client accepts content in the application/rdf+xml format.

  6. 6.

    http://allrecipes.com/Recipe/Veggie-Pizza.

  7. 7.

    http://www.alchemyapi.com/.

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Acknowledgments

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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Authors and Affiliations

  1. University of Innsbruck, STI Innsbruck, Technikerstr. 21a, 6020, Innsbruck, Austria

    Ioannis Stavrakantonakis, Anna Fensel & Dieter Fensel

Authors
  1. Ioannis Stavrakantonakis
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  2. Anna Fensel
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  3. Dieter Fensel
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Corresponding author

Correspondence to Ioannis Stavrakantonakis .

Editor information

Editors and Affiliations

  1. Southeast University, Nanjing, China

    Guilin Qi

  2. Osaka University, Ibaraki, Japan

    Kouji Kozaki

  3. The University of Aberdeen, Aberdeen, United Kingdom

    Jeff Z. Pan

  4. Zhongnan Hospital of Wuhan University, Wuhan, China

    Siwei Yu

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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. https://doi.org/10.1007/978-3-319-31676-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-31676-5_3

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