Towards better measures: evaluation of estimated resource description quality for distributed IR

Mark Baillie, Leif Azzopardi, Fabio Crestani

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

5 Citations (Scopus)


An open problem for Distributed Information Retrieval systems (DIR) is how to represent large document repositories, also known as resources, both accurately and efficiently. Obtaining resource description estimates is an important phase in DIR, especially in non-cooperative environments. Measuring the quality of an estimated resource description is a contentious issue as current measures do not provide an adequate indication of quality. In this paper, we provide an overview of these currently applied measures of resource description quality, before proposing the Kullback-Leibler (KL) divergence as an alternative. Through experimentation we illustrate the shortcomings of these past measures, whilst providing evidence that KL is a more appropriate measure of quality. When applying KL to compare different QBS algorithms, our experiments provide strong evidence in favour of a previously unsupported hypothesis originally posited in the initial Query-Based Sampling work.
Original languageEnglish
Title of host publicationInfoScale '06 Proceedings of the 1st International Conference on Scalable Information Systems
Place of PublicationNew York, NY, USA
Number of pages8
Publication statusPublished - 30 May 2006

Publication series

NameInfoScale '06


  • information retrieval
  • search engine optimization
  • retrieval evaluation


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