Breaking Out The XML MisMatch Trap

TL;DR

This paper addresses the XML keyword search MisMatch problem by proposing a practical detection and suggestion method that improves user query refinement, demonstrating effectiveness and efficiency through extensive experiments and an online search engine implementation.

Contribution

It introduces a novel approach for detecting and suggesting solutions to the XML MisMatch problem, enhancing query refinement in keyword search systems.

Findings

Effective detection of MisMatch problem in XML keyword search

Generation of helpful query suggestions and sample results

Demonstrated scalability and efficiency on real datasets

Abstract

In keyword search, when user cannot get what she wants, query refinement is needed and reason can be various. We first give a thorough categorization of the reason, then focus on solving one category of query refinement problem in the context of XML keyword search, where what user searches for does not exist in the data. We refer to it as the MisMatch problem in this paper. Then we propose a practical way to detect the MisMatch problem and generate helpful suggestions to users. Our approach can be viewed as a post-processing job of query evaluation, and has three main features: (1) it adopts both the suggested queries and their sample results as the output to user, helping user judge whether the MisMatch problem is solved without consuming all query results; (2) it is portable in the sense that it can work with any LCA-based matching semantics and orthogonal to the choice of result retrieval method adopted; (3) it is lightweight in the way that it occupies a very small proportion of the whole query evaluation time. Extensive experiments on three real datasets verify the effectiveness, efficiency and scalability of our approach. An online XML keyword search engine called XClear that embeds the MisMatch problem detector and suggester has been built.