Improving Retrieval Results with discipline-specific Query Expansion

TL;DR

This paper investigates the use of discipline-specific Search-Term-Recommendation systems for query expansion, demonstrating improved retrieval performance when using tailored STRs over general ones, though automatic selection remains challenging.

Contribution

It introduces and evaluates discipline-specific STRs for query expansion, showing their effectiveness over general recommendations and highlighting the challenge of automatic selection.

Findings

Discipline-specific STRs improve retrieval results significantly.

Heuristic-based selection of STRs outperforms general STRs.

Automatic matching of the best STR remains a major challenge.

Abstract

Choosing the right terms to describe an information need is becoming more difficult as the amount of available information increases. Search-Term-Recommendation (STR) systems can help to overcome these problems. This paper evaluates the benefits that may be gained from the use of STRs in Query Expansion (QE). We create 17 STRs, 16 based on specific disciplines and one giving general recommendations, and compare the retrieval performance of these STRs. The main findings are: (1) QE with specific STRs leads to significantly better results than QE with a general STR, (2) QE with specific STRs selected by a heuristic mechanism of topic classification leads to better results than the general STR, however (3) selecting the best matching specific STR in an automatic way is a major challenge of this process.