Interactive query expansion for professional search applications
This work addresses the need for improved search efficiency for knowledge workers like healthcare professionals and patent agents, though it is incremental in applying existing methods to a specialized context.
The paper tackled the problem of supporting complex, expert-driven search tasks in professional domains by evaluating query expansion methods on real-world Boolean search strategies, finding that context-free distributional language models and ngram order cues effectively balance precision and recall.
Knowledge workers (such as healthcare information professionals, patent agents and recruitment professionals) undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expert knowledge to formulate accurate search strategies. Interactive features such as query expansion can play a key role in supporting these tasks. However, generating query suggestions within a professional search context requires that consideration be given to the specialist, structured nature of the search strategies they employ. In this paper, we investigate a variety of query expansion methods applied to a collection of Boolean search strategies used in a variety of real-world professional search tasks. The results demonstrate the utility of context-free distributional language models and the value of using linguistic cues such as ngram order to optimise the balance between precision and recall.