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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.
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.
Robust and distributed web-scale near-dup document conflation in microsoft academic service
TLDR
A robust and distributed framework to perform conflation on noisy data in the Microsoft Academic Service dataset is proposed and the result shows that the algorithm can conflate noisy data robustly and efficiently.
Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature
TLDR
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.
In Search for a Cure: Recommendation With Knowledge Graph on CORD-19
TLDR
This hands-on tutorial targets at providing a comprehensive and pragmatic end-to-end walk-through for building an academic research paper recommender for the use case of COVID-19 related study, with the help of knowledge graph technology.
SciConceptMiner: A system for large-scale scientific concept discovery
Scientific knowledge is evolving at an unprecedented rate of speed, with new concepts constantly being introduced from millions of academic articles published every month. In this paper, we introduce
Explainable and Sparse Representations of Academic Articles for Knowledge Exploration
TLDR
On these applications, it is revealed that the knowledge encoded in the tagging system can be effectively utilized and can help infer additional features from data with limited information.