Exploring Query Categorisation for Query Expansion: A Study

TL;DR

This paper proposes a taxonomy of query classes to improve query expansion effectiveness by tailoring techniques to specific query types, aiming to address vocabulary mismatch in information retrieval.

Contribution

It introduces a novel taxonomy of query categories from a QE perspective and discusses tailored strategies for each category, advancing personalized query expansion methods.

Findings

Proposed a taxonomy of query classes with properties and examples

Discussed potential QE strategies for each query category

Lays groundwork for automatic query categorisation and testing on datasets

Abstract

The vocabulary mismatch problem is one of the important challenges facing traditional keyword-based Information Retrieval Systems. The aim of query expansion (QE) is to reduce this query-document mismatch by adding related or synonymous words or phrases to the query. Several existing query expansion algorithms have proved their merit, but they are not uniformly beneficial for all kinds of queries. Our long-term goal is to formulate methods for applying QE techniques tailored to individual queries, rather than applying the same general QE method to all queries. As an initial step, we have proposed a taxonomy of query classes (from a QE perspective) in this report. We have discussed the properties of each query class with examples. We have also discussed some QE strategies that might be effective for each query category. In future work, we intend to test the proposed techniques using standard datasets, and to explore automatic query categorisation methods.