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.
apply four methods for microblog retrieval @cite_10 : query reformulation, automatic query expansion, affinity propagation as well as a combination of these techniques. To reformulate the query hashtags are extracted from tweets and used as additional information for the query. Furthermore, every two consecutive words of the query are grouped and added to the query. A relevance feedback model is used for automatic query expansion. The respective top ten terms of the top ten documents are selected. The affinity propagation approach is implemented by using a cluster algorithm to group tweets. The idea behind this is that the probability of tweets being relevant is higher for those, which are similar to relevant tweets. It is shown that automatic query expansion is a very effective method, while affinity propagation is less successful.
@cite_10
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This report describes the methods that our Information Retrieval Group at Purdue University used for the TREC Microblog 2011 track. The rst method is the pseudo-relevance feedback, a traditional algorithm to reformulate the query by adding expanded terms to the query. The second method is the anity propagation, a non parametric clustering algorithm that can group the top tweets according to their similarities. The nal score of a tweet is based on its relevance score and the relevance score of its representative in the group. We found that query expansion is a very useful technique for microblog retrieval, while anity propagation could achieve a comparable performance when combining with other techniques.