Discovery by scent: Discovery browsing system based on the Information Foraging Theory

This work presents a discovery browsing system based on the Information Foraging Theory (IFT). Discovery browsing is a type of information seeking behavior where the expert user interacts iteratively with a literature-based discovery system to explore poorly understood relationships with the end goal of formulating a hypothesis or gaining insight by uncovering novel points of view. The mathematical model underlying the IFT is predictive of information seeking behavior of foragers on the World Wide Web (WWW) in a plethora of scenarios. We hypothesize that a discovery browsing system based upon the IFT can assist the user in the process of discovery by automatically making available the concepts to which the user would most likely attend. Given initial terms from a user, the discovery browsing system mines a semantic network of over 26 million object-relation-object pairs from 7.8 million MEDLINE citations and presents a ranked sub-graph, which is the prediction of where the interesting concepts (ideally discoveries) lie. In this work, we present the theoretical foundations and design of the discovery browsing system. To demonstrate its efficacy, we replicate two recent discoveries and demonstrate that it is able to predict the concepts that were determined as playing a role in novel hypotheses proposed by scientists.