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An Overview of Microsoft Academic Service (MAS) and Applications
A knowledge driven, highly interactive dialog that seamlessly combines reactive search and proactive suggestion experience, and a proactive heterogeneous entity recommendation are demonstrated.
CORD-19: The COVID-19 Open Research Dataset
The mechanics of dataset construction are described, highlighting challenges and key design decisions, an overview of how CORD-19 has been used, and several shared tasks built around the dataset are described.
A Review of Microsoft Academic Services for Science of Science Studies
The use of three key AI technologies that underlies its prowess in capturing scholarly communications with adequate quality and broad coverage are focused on, including a reinforcement learning approach to assessing scholarly importance for entities participating in scholarly communications, called the saliency, that serves both as an analytic and a predictive metric in MAS.
A Scalable Hybrid Research Paper Recommender System for Microsoft Academic
There is a strong correlation between participant scores and the similarity rankings produced by the large scale hybrid paper recommender system but that additional focus needs to be put towards improving recommender precision, particularly for content based recommendations.
Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature
The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary.