Information overload has led to a situation where users are swamped with too much information, resulting in difficulty sifting through material in search of relevant content. Aims to address this issue from the perspective of collaborative querying, an approach that helps users formulate queries by harnessing the collective knowledge of other searchers.
The design and implementation of the Query Graph Visualizer (QGV), a collaborative querying system which harvests and clusters previously issued queries to form query networks that represent related information needs are described. A preliminary evaluation of the QGV is also described in which a group of participants evaluated the usability and usefulness of the system by completing a set of tasks and a questionnaire based on Nielsen's heuristic evaluation technique.
In the QGV, a submitted query is matched to its closest cluster and a recursive algorithm is applied to find other related clusters, forming a query network. The queries in the network are explored in the QGV, helping users locate other queries that might meet their current information needs. The results of the evaluation suggest the usefulness and usability of the system. Participants could complete their assigned tasks using the QGV and positively rated the system in terms of usability.
The techniques described can be used to design information retrieval systems that learn from the trials and tribulations of other searchers and help users in their quest for relevant and quality information
