查看文章

Knowledge workers (such as healthcare information professionals, patent agents and media monitoring professionals) need to create and execute search strategies that are accurate, repeatable and transparent. The traditional solution is to use lineby-line ‘query builders’ such as those offered by proprietary database vendors. However, these offer limited support for error checking or query optimization, and their output can often be compromised by errors and inefficiencies. In this paper, we present a new approach to query formulation in which concepts are expressed as objects on a two-dimensional canvas. Relationships between objects are articulated by manipulating them using drag and drop. Automated search term suggestions are provided using a combination of knowledge-based and statistical natural language processing techniques. This approach has the potential to eliminate many sources of inefficiency, make the query semantics more transparent, and offers further opportunities for query refinement and optimisation.