A Study: From Electronic Laboratory Notebooks to Generated Queries for Literature Recommendation

Relating one’s research to the vast body of scientific knowledge is a difficult task; the sheer volume of literature makes it difficult to keep up-to-date with scientific developments. Particularly when research is on-going, keeping track of related work is especially important to avoid an unintended duplication of effort. We outline a novel approach to this problem that uses the text in an Electronic Laboratory Notebook (ELN) as a representation of an experimental context in the field of Chemistry. The contribution of this work is to situate the literature recommendation task within the context of the user’s experimental information needs. We find that our approach to transform the ELN text into queries for use with PubMed is able to recover a subset of user bibliographies. We find that alternative methods for query generation that capture both scientific terminology and salient terms in the ELN complement each other.