Segmenting and Labeling Query Sequences in a Multidatabase Environment

Abstract

When gathering information from multiple independent data sources, users will generally pose a sequence of queries to each source, combine (union) or cross-reference (join) the results in order to obtain the information they need. Furthermore, when gathering information, there is a fair bit of trial and error involved, where queries are recursively refined according to the results of a previous query in the sequence. From the point of view of an outside observer, the aim of such a sequence of queries may not be immediately obvious. We investigate the problem of isolating and characterizing subsequences representing coherent information retrieval goals out of a sequence of queries sent by a user to different data sources over a period of time. The problem has two sub-problems: segmenting the sequence into subsequences, each representing a discrete goal; and labeling each query in these subsequences according to how they contribute to the goal. We propose a method in which a discriminative probabilistic model (a Conditional Random Field) is trained with pre-labeled sequences. We have tested the accuracy with which such a model can infer labels and segmentation on novel sequences. Results show that the approach is very accurate (> 95% accuracy) when there are no spurious queries in the sequence and moderately accurate even in the presence of substantial noise (∼70% accuracy when 15% of queries in the sequence are spurious)