XML Retrieval by Improving Structural Relevance Measures Obtained from Summary Models

Abstract

In XML retrieval, there is often more than one element in the same document that could represent the same focused result. So, a key challenge for XML retrieval systems is to return the set of elements that best satisfies the information need of the end-user in terms of both content and structure. At INEX, there have been numerous proposals for how to incorporate structural constraints and hints into ranking. These proposals either boost the score of or filter out elements that have desirable structural properties. An alternative approach that has not been explored is to rank elements by improving their structural relevance. Structural relevance is the expected relevance of a list of elements, based on a graphical model of how users browse elements within documents. In our approach, we use summary graphs to describe the process of a user browsing from one part of a document to another.

In this paper, we develop an algorithm to structurally score retrieval scenarios using structural relevance. The XML retrieval system identifies the candidate scenarios. We apply structural relevance with a given summary model to identify the most structurally relevant scenario. This results in improved system performance. Our approach provides a consistent way to apply different user models to ranking. We also explore the use of score boosting using these models.