Supporting Scholarly Search with Keyqueries

We deal with a problem faced by scholars every day: identifying relevant papers on a given topic. In particular, we focus on the scenario where a scholar can come up with a few papers (e.g., suggested by a colleague) and then wants to find “all” the other related publications. Our proposed approach to the problem is based on the concept of keyqueries: formulating keyqueries from the input papers and suggesting the top results as candidates of related work. We compare our approach to three baselines that also represent the different ways of how humans search for related work: (1) a citation-graph-based approach focusing on cited and citing papers, (2) a method formulating queries from the paper abstracts, and (3) the “related articles”-functionality of Google Scholar. The effectiveness is measured in a Cranfield-style user study on a corpus of 200,000 papers. The results indicate that our novel keyquery-based approach is on a par with the strong citation and Google Scholar baselines but with substantially different results—a combination of the different approaches yields the best results.