Microsoft Academic

Microsoft Academic

Established: February 22, 2016

Next Steps for Microsoft Academic – Expanding into New Horizons

May 4, 2021

Editor’s note, June 4, 2021 – the post has been updated with a more extensive FAQ to provide more details on the changes announced May 4.

For over seven years, Microsoft Research has been proud to have one of its AI research projects contribute to the open exchange of knowledge within the research community. We are now evolving our focus to explore how we can advance these AI technologies in Microsoft 365 to empower every person and organization to derive valuable insights from their content.

We remain confident in open and community-driven alternatives to MAS and are pleased to see the recent momentum across the academic ecosystem. Many of our open-source machine learning algorithms and annotated data repositories are available to the community today, and we will continue to provide guidance to key partners throughout this transition.

Microsoft Academic has been on a mission to explore new ways to empower researchers and research organizations to achieve more. The research project is characterized by two sets of technologies: one that reads all the Bing-indexed web pages and organizes the most up-to-date academic knowledge into a knowledge base called Microsoft Academic Graph (MAG), and the other that performs semantic reasoning and inference to serve that knowledge through the Microsoft Academic search website and API. We are proud that these data and web services have been found useful in numerous research projects around the world, and excited to see more community-driven, public efforts emerge.

One question that we are asked frequently, though, is how the technologies powering Microsoft Academic can be used by institutions outside of academia to make organizational knowledge more discoverable and accessible. Over the years, we have openly shared some of the building blocks, such as the language and network similarity packages, and the core search engine MAKES.  With the continued progress in data access, we believe now is the right time to fully explore opportunities to extend this technology to new industries and transition to community approaches for academic research.

Microsoft Research will continue to support the automated AI agents powering Microsoft Academic services through the end of calendar year 2021. During this time, we encourage existing Microsoft Academic users to begin transitioning to other equivalent services. Below are just a few of the many great options available to the community.

Thank you very much for the years of support and encouragement. We are immensely grateful to have learned and grown from your feedback over the years. As we are passing the torch to the community-driven efforts, we invite you to join us in continuously contributing ideas and suggestions to nurture, embrace, and grow these platforms.

FAQ on Microsoft Academic

Q: What is happening to Microsoft Academic Services (MAS)?
A: Microsoft Research set out to demonstrate AI-curated knowledge can effectively assist people in making serendipitous discoveries and deriving valuable insights. After seven years of developing the machine reading technology and working with the research community, we have chosen to embrace a community-driven approach within academia and now turn our focus to exploring ways we can extend this technology to even more people and organizations. This AI research project will be supported until the end of calendar year 2021, upon which time MAS will be retired.

What this means for each service:

  • Microsoft Academic Website: No longer accessible after Dec. 31, 2021
  • Project Academic Knowledge: No longer accessible after Dec. 31, 2021
  • Microsoft Academic Graph/Microsoft Academic Knowledge Exploration Service: No longer providing updated data or access to old releases after Dec. 31, 2021; however, existing copies can still be used under license.

Q: Why is Microsoft retiring MAS?
A: Microsoft Research developed MAS in response to feedback from our colleagues that the inequality in accessing large datasets presented a significant obstacle to conducting research and cultivating academic talents in the areas of Big Data and AI. With MAS, Microsoft Research has been proud to contribute to a culture of open exchange and a growing ecosystem of collaborators. As this research project has achieved its objective to remove the data access barriers for our research colleagues, it is the right time to explore other opportunities to give back to communities outside of academia.

Q: What will happen to the customers using the research service?
A: Customers are welcome to continue their use of MAS following the same data licenses and terms of use until the end of calendar year 2021

Q*. Can customers pay to keep MAS running indefinitely?
A. The decision for the team to move on from MAS is not based on the operational but on the opportunity cost. We recognize the core mission of MAS, to have intelligent agents gather knowledge and empower humans to gain deeper insights and make better decisions, are not only useful to the academic but also to all modern workers and students. Expanding our scope to areas beyond academic contents, particularly in the enterprise and the educational settings where our work can serve many orders of magnitude more users, is a tremendous opportunity with exciting challenges. Besides, the momentum is gaining on an open and community-driven alternative to MAS. We expect a few will be available by the end of the year, and the first public announcement of a MAG replacement and this announcement have just bolstered the confidence in this assessment.

Q*. Can you open-source components that create MAS?
A. The portion of the software that implements machine learning algorithms has been publicly disclosed in detail . Additionally, for modern machine learning, a large amount of annotated data can arguably be more valuable than software for implementing known algorithms. MAG has published such annotated data, in some advanced cases, with the confidence scores describing the qualities of the annotations. We have open-sourced examples of leveraging MAG annotations in modern Python-based machine learning frameworks, for example, a graph representation learning approach called HGT at this GitHub repository and an advanced topic recognition algorithm called MATCH here.

Q*. Can you provide a high-level sketch of the software architecture for MAS?
A. We have shared with our partners this deck that visualizes the architecture described in our published articles (links in the deck). We hope you find the illustrations helpful as well.