Query Expansion for Survey Question Retrieval in the Social Sciences
This work addresses the challenge of survey question reuse in digital libraries for social science researchers, though it is incremental as it builds on existing query expansion methods.
The paper tackled the problem of retrieving social science survey questions by improving query formulation through query expansion, showing that automatically expanded queries using co-occurring terms outperformed both manually reformulated queries by a domain expert and a keyword-based BM25 baseline.
In recent years, the importance of research data and the need to archive and to share it in the scientific community have increased enormously. This introduces a whole new set of challenges for digital libraries. In the social sciences typical research data sets consist of surveys and questionnaires. In this paper we focus on the use case of social science survey question reuse and on mechanisms to support users in the query formulation for data sets. We describe and evaluate thesaurus- and co-occurrence-based approaches for query expansion to improve retrieval quality in digital libraries and research data archives. The challenge here is to translate the information need and the underlying sociological phenomena into proper queries. As we can show retrieval quality can be improved by adding related terms to the queries. In a direct comparison automatically expanded queries using extracted co-occurring terms can provide better results than queries manually reformulated by a domain expert and better results than a keyword-based BM25 baseline.