Hierarchical link analysis for ranking web data

  • Authors:

  • Renaud Delbru;Nickolai Toupikov;Michele Catasta;Giovanni Tummarello;Stefan Decker

  • Affiliations:

  • Digital Enterprise Research Institute, National University of Ireland, Galway, Galway, Ireland;Digital Enterprise Research Institute, National University of Ireland, Galway, Galway, Ireland;School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;,Digital Enterprise Research Institute, National University of Ireland, Galway, Galway, Ireland;Digital Enterprise Research Institute, National University of Ireland, Galway, Galway, Ireland

  • Venue:
  • ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
  • Year:
  • 2010

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Abstract

On the Web of Data, entities are often interconnected in a way similar to web documents. Previous works have shown how PageRank can be adapted to achieve entity ranking. In this paper, we propose to exploit locality on the Web of Data by taking a layered approach, similar to hierarchical PageRank approaches. We provide justifications for a two-layer model of the Web of Data, and introduce DING (Dataset Ranking) a novel ranking methodology based on this two-layer model. DING uses links between datasets to compute dataset ranks and combines the resulting values with semantic-dependent entity ranking strategies. We quantify the effectiveness of the approach with other link-based algorithms on large datasets coming from the Sindice search engine. The evaluation which includes a user study indicates that the resulting rank is better than the other approaches. Also, the resulting algorithm is shown to have desirable computational properties such as parallelisation.