An Open-Access Platform for Transparent and Reproducible Structured Searching

Knowledge workers such as patent agents, recruiters and legal researchers undertake work tasks in which search forms a core part of their duties. In these instances, the search task often involves formulation of complex queries expressed as Boolean strings. However, creating effective Boolean queries remains an ongoing challenge, often compromised by errors and inefficiencies. In this paper, we demonstrate a new approach to structured searching in which concepts are expressed as objects on a two-dimensional canvas. Interactive query suggestions are provided via an NLP services API, and support is offered for optimising, translating and sharing search strategies as executable artefacts. This eliminates many sources of error, makes the query semantics more transparent, and offers an open-access platform for sharing reproducible search strategies and best practices.