1. Proceedings
  2. I3M
  3. 2019
  4. VARE
  5. 10.46354/i3m.2019.vare.009

Intelligent navigation of linked data with a graphical interface based on semantic similarity

  • a Pedro Fonseca-Ortiz  , 
  • b Ceballos H. G.  
  • abTecnológico de Monterrey, Mexico
Cite as
Fonseca-Ortiz P., Ceballos H. (2019). Intelligent navigation of linked data with a graphical interface based on semantic similarity. Proceedings of the 5th International Conference on Virtual and Augmented Reality in Education (VARE 2019), pp. 56-61. DOI: https://doi.org/10.46354/i3m.2019.vare.009

Abstract

Semantic Web Technology proposes the use of linked data and ontologies as a mean for providing meaning to information. Even though several tools for the analysis and visualization of linked data exist, these tools require a lot of specialized knowledge to fulfill a purpose. Additionally, this complexity hardens its use for nonexperienced users therefore limiting semantic web applications. This paper describes a tool that combines the use of a recommendation system and an intuitive dynamic user interface for navigating linked data. The tool guides the user to find resources of interest by highlighting those related to his search intention. This is, the platform learns on the fly the user interest and makes recommendations based on the connections between resources.

References

  1. Allemang, Dean and James Hendler (2008). Semantic Web for the Working Ontologist: Effective
    Modeling in RDFS and OWL. San. Francisco, Ca, USA: Morgan Kaufmann Publishers Inc.
  2. Berners-Lee, Tim & Hendler, James & Lassila, Ora.(2001). The Semantic Web: A New Form of Web Content That is Meaningful to Computers Will Unleash a Revolution of New Possibilities.
    ScientificAmerican.com.
  3. Bizer, C., Heath, T., & Berners-Lee, T. (2011). Linked Data: The Story so Far. In A. Sheth (Ed.),
    Semantic Services, Interoperability and Web Applications: Emerging Concepts (pp. 205-227).
    Hershey, PA: IGI Global.
  4. Feigenbaum, L., Herman, I., Hongsermeier, T., Neumann, E., & Stephens, S. (2007). The semantic
    web in action. Scientific American, 297(6), 90-97.
  5. Carlos A. Gomez-Uribe and Neil Hunt. 2015. The Netflix Recommender System: Algorithms,
    Business Value, and Innovation. ACM Trans. Manage. Inf. Syst. 6, 4, Article 13 (December 2015), 19 pages.
  6. Hanani, U., Shapira, B., & Shoval, P. (2001). Information Filtering: Overview of Issues,
    Research and Systems. User Modeling and User-Adapted Interaction, 11, 203-259.
  7. Nazemi, K. (2016). Adaptive semantics visualization (Vol. 646). Basel: Springer.
  8. Kawa Nazemi, Dirk Burkhardt, Egils Ginters, Jorn Kohlhammer, Semantics Visualization –
    Definition, Approaches and Challenges, Procedia Computer Science, Volume 75, 2015, Pages 75-83.
  9. Nazemi K., Burkhardt D., Retz R., Kuijper A., Kohlhammer J. (2014) Adaptive Visualization of
    Linked-Data. In: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2014. Lecture Notes in
    Computer Science, vol 8888. Springer, Cham
  10. Qiu, T., Chen, G., Zhang, Z. K., & Zhou, T. (2011). An item-oriented recommendation algorithm on coldstart problem. EPL (Europhysics Letters), 95(5), 58003.
  11. (1999). “Resource Description Framework (RDF) Model and Syntax Specification”, W3C. URL:
    http://www.w3.org/TR/PR-rdf-syntax/.
  12. (2013). “W3C Semantic Web Activity, World Wide Web Consortium (W3C)”. World Wide Web
    Consortium (W3C)”. [].
  13. (1999). “Resource Description Frame- work (RDF) Model and Syntax Specification”, “W3C-REC”.
    URL: http://www.w3.org/TR/PR-rdf-syntax/.