Crowdsourcing Scholarly Data

One of the promises of Web Science is to leverage the wisdom of the crowds to give rise to emergent, bottom-up semantics, by making it easy for users to express relationships between arbitrary kinds of objects. Rather than starting with an ontology that determines the kinds of objects and relationships to be described and reasoned about, the idea is to give users the freedom to annotate arbitrary objects with arbitrary predicates, along with incentives for such annotations. Social tagging systems for images are one example, where the motivation can stem from the wish to organize and share one’s photos or from entertaining games to guess one another’s tags. Here we explore a similar approach in the domain of scholarly publications. We describe a system called Scholarometer, which provides a service to scholars by computing citation-based impact measures. This motivates users to provide disciplinary annotations for authors, which in turn can be used to compute for the first time measures that allow to compare authors’ impact across disciplinary boundaries. We show how this crowdsourcing approach can lead to emergent semantic networks to study interdisciplinary annotations and trends.