Relevance Feedback and Inference Networks

Abstract

Relevance feedback, which modifies queries using judgements of the relevance of a few, highly-ranked documents, has historically been an important method for increasing the performance of information retrieval systems. In this paper, we extend the inference network model introduced by Turtle and Croft to include relevance feedback techniques. The difference between relevance feedback on text abstracts and full text collections is studied. Preliminary results for relevance feedback on the structured queries supported by the inference net model are also reported. 1 Introduction Relevance feedback methods in information retrieval attempt to improve performance for a particular query by modifying the query, based on the user's reaction to the initial retrieved documents. Specifically, the user's judgements of the relevance or non-relevance of some of the documents retrieved are used to add new terms to the query and to reweight query terms. For example, if all the documents, that the user..