Introducing lateral thinking in search engines

Decomposing a very complex problem into smaller subproblems that are much easier to solve is not a new idea. The “Parisian Approach”[9] applies this principle extensively to shatter complexity by cutting down the original problem into many small subproblems that are then globally optimized thanks to an evolutionary algorithm. This paper describes how this approach has been used to interactively evolve a user profile to be used by a search engine. User queries are rewritten thanks to the evolved profile, resulting in an increased diversity in the retrieved documents that is showing an interesting property: even though precision is lost, retrieved documents relate both to the user’s query and to his areas of interest in a manner that evokes “lateral thinking”. This paper describes ELISE, an Evolutionary Learning Interactive Search Engine that interactively evolves rewriting modules and rules (some kind of elaborated user profile) along a Parisian Approach. Results obtained over a public domain benchmark (Cystic Fibrosis Database) are presented and discussed. This research is partly funded by Novartis-Pharma (IK@N/KE)