In this paper, we describe the WSDM Cup entity ranking challenge held in conjunction with the 2016 Web Search and Data Mining conference (WSDM 2016). Participants in the challenge were provided access to the Microsoft Academic Graph (MAG), a large heterogeneous graph of academic entities, and were invited to calculate the query-independent importance of each publication in the graph. Submissions for the challenge were open from August through November 2015, and a public leaderboard displayed teams? progress against a set of training judgements. Final evaluations were performed against a separate, withheld portion of the evaluation judgements. The top eight performing teams were then invited to submit papers to the WSDM Cup workshop, held at the WSDM 2016 conference.

BACKGROUND: In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles. RESULTS: The Word add-in presented here will assist authors in this effort by automatically recognizing and highlighting words or phrases that are likely information-rich, allowing authors to associate semantic data with those words or phrases, and to embed that data in the document as XML. The add-in and source code are publicly available at http://www.codeplex.com/UCSDBioLit. CONCLUSIONS: The Word add-in for ontology term recognition makes it possible for an author to add semantic data to a document as it is being written and it encodes these data using XML tags that are effectively a standard in life sciences literature. Allowing authors to mark-up their own work will help increase the amount and quality of machine-readable literature metadata.