English

The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets

Social and Information Networks 2019-05-29 v1 Databases Human-Computer Interaction

Abstract

Traditional verbatim browsers give back information in a linear way according to a ranking performed by a search engine that may not be optimal for the surfer. The latter may need to assess the pertinence of the information retrieved, particularly when s\cdothe wants to explore other facets of a multi-facetted information space. For instance, in a multimedia dataset different facets such as keywords, authors, publication category, organisations and figures can be of interest. The facet simultaneous visualisation can help to gain insights on the information retrieved and call for further searches. Facets are co-occurence networks, modeled by HyperBag-Graphs -- families of multisets -- and are in fact linked not only to the publication itself, but to any chosen reference. These references allow to navigate inside the dataset and perform visual queries. We explore here the case of scientific publications based on Arxiv searches.

Keywords

Cite

@article{arxiv.1905.11695,
  title  = {The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets},
  author = {Xavier Ouvrard and Jean-Marie Le Goff and Stéphane Marchand-Maillet},
  journal= {arXiv preprint arXiv:1905.11695},
  year   = {2019}
}

Comments

Extension of the hypergraph framework shortly presented in arXiv:1809.00164 (possible small overlaps); use the theoretical framework of hb-graphs presented in arXiv:1809.00190

R2 v1 2026-06-23T09:28:33.384Z