• Publications
  • Influence
An Overview of Microsoft Academic Service (MAS) and Applications
TLDR
A knowledge driven, highly interactive dialog that seamlessly combines reactive search and proactive suggestion experience, and a proactive heterogeneous entity recommendation are demonstrated. Expand
CORD-19: The COVID-19 Open Research Dataset
TLDR
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. Expand
Microsoft Academic Graph: When experts are not enough
TLDR
The design, schema, and technical and business motivations behind MAG are described and how MAG can be used in analytics, search, and recommendation scenarios are elaborated. Expand
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
TLDR
A novel self-supervised framework, named TaxoExpan, which automatically generates a set of ⟨query concept, anchor concept⟩ pairs from the existing taxonomy as training data, and develops two innovative techniques, including a position-enhanced graph neural network that encodes the local structure of an anchor concept in theexisting taxonomy and a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self- supervision data. Expand
A Review of Microsoft Academic Services for Science of Science Studies
TLDR
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. Expand
A Web-scale system for scientific knowledge exploration
TLDR
This work presents a large-scale system to identify hundreds of thousands of scientific concepts, tag these identified concepts to hundreds of millions of scientific publications, and build a six-level concept hierarchy with a subsumption-based model. Expand
User Fatigue in Online News Recommendation
TLDR
By analyzing user behavioral logs from Bing Now news recommendation, it is found that user fatigue is a severe problem that greatly affects the user experience and experimental results indicate that significant gains can be achieved by introducing features that reflect users' interaction with previously seen recommendations. Expand
A Scalable Hybrid Research Paper Recommender System for Microsoft Academic
TLDR
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. Expand
F2R: Publishing file systems as Linked Data
TLDR
F2R is presented, a lightweight system for exposing file systems as Linked Data and automatically linking files to DBpedia or external sources and proposes four kinds of file metadata to enrich the information of files and adopt semantic web tools to automatically link to other sources, which enables new possibilities of the Web-based data integration and semantic actions. Expand
A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations
TLDR
It is found that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s. Expand
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