Improving Retrieval Results with discipline-specific Query Expansion

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.

6 pages; to be published in Proceedings of Theory and Practice of Digital Libraries 2012 (TPDL 2012)

References in corpus (1)

• Science Models as Value-Added Services for Scholarly Information Systems

Cited by in corpus (5)

• A Usefulness-based Approach for Measuring the Local and Global Effect of IIR Services
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• Recognizing Topic Change in Search Sessions of Digital Libraries based on Thesaurus and Classification System
• User Interests in German Social Science Literature Search - A Large Scale Log Analysis
• A System for Probabilistic Linking of Thesauri and Classification Systems