A Topic-Based Measure of Resource Description Quality for Distributed Information Retrieval

The aim of query-based sampling is to obtain a sufficient, representative sample of an underlying (text) collection. Current measures for assessing sample quality are too coarse grain to be informative. This paper outlines a measure of finer granularity based on probabilistic topic models of text. The assumption we make is that a representative sample should capture the broad themes of the underlying text collection. If these themes are not captured, then resource selection will be affected in terms of performance, coverage and reliability. For example, resource selection algorithms that require extrapolation from a small sample of indexed documents to determine which collections are most likely to hold relevant documents may be affected by samples which do not reflect the topical density of a collection. To address this issue we propose to measure the relative entropy between topics obtained in a sample with respect to the complete collection. Topics are both modelled from the collection and inferred in the sample using latent Dirichlet allocation. The paper outlines an analysis and evaluation of this methodology across a number of collections and sampling algorithms.