Interactive query expansion for professional search applications

Russell-Rose, Tony; Gooch, Philip and Kruschwitz, Udo. 2021. Interactive query expansion for professional search applications. Business Information Review, 38(3), pp. 127-137. ISSN 0266-3821 [Article]

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Abstract or Description

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 to optimise the balance between precision and recall.

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Additional Information:

This research was supported by Innovate UK Open Competition R&D grant 102975, ‘Intelligent Search Assistance’.

Test data is publicly available via Github. Evaluation code is hosted on BitBucket and can be made available on demand.


Information retrieval, machine learning, natural language processing, ontologies, professional search, query expansion

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September 2021Published
23 July 2021Published Online
29 June 2021Accepted

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Date Deposited:

27 Jul 2021 14:10

Last Modified:

22 Nov 2021 11:06

Peer Reviewed:

Yes, this version has been peer-reviewed.


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