IDEAS home Printed from https://ideas.repec.org/a/igg/jswis0/v13y2017i4p134-154.html
   My bibliography  Save this article

Allied: A Framework for Executing Linked Data-Based Recommendation Algorithms

Author

Listed:
  • Cristhian Figueroa

    (Politecnico di Torino, Torino, Italy & Universidad del Cauca, Popayan, Colombia)

  • Iacopo Vagliano

    (Politecnico di Torino, Torino, Italy)

  • Oscar Rodriguez Rocha

    (INRIA Sophia Antipolis MNRIA Sophia, Sophia Antipolis, France)

  • Marco Torchiano

    (Politecnico di Torino, Torino, Italy)

  • Catherine Faron Zucker

    (Universitf Nice Sophia Antipolis, Sophia Antipolis, France)

  • Juan Carlos Corrales

    (Universidad del Cauca, Popayan, Colombia)

  • Maurizio Morisio

    (Politecnico di Torino, Torino, Italy)

Abstract

The increase in the amount of structured data published on the Web using the principles of Linked Data means that now it is more likely to find resources on the Web of Data that represent real life concepts. Discovering and recommending resources on the Web of Data related to a given resource is still an open research area. This work presents a framework to deploy and execute Linked Data based recommendation algorithms to measure their accuracy and performance in different contexts. Moreover, application developers can use this framework as the main component for recommendation in various domains. Finally, this paper describes a new recommendation algorithm that adapts its behavior dynamically based on the features of the Linked Data dataset used. The results of a user study show that the algorithm proposed in this paper has better accuracy and novelty than other state-of-the-art algorithms for Linked Data.

Suggested Citation

  • Cristhian Figueroa & Iacopo Vagliano & Oscar Rodriguez Rocha & Marco Torchiano & Catherine Faron Zucker & Juan Carlos Corrales & Maurizio Morisio, 2017. "Allied: A Framework for Executing Linked Data-Based Recommendation Algorithms," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(4), pages 134-154, October.
    • Handle: RePEc:igg:jswis0:v:13:y:2017:i:4:p:134-154
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSWIS.2017100107
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jswis0:v:13:y:2017:i:4:p:134-154. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.