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Hurry up to use the Microsoft Academic search engine

Recently, Microsoft announced that it will shut down Microsoft Academic, the second largest academic search engine after Google Scholar. Although the global scientific community took little notice of this announcement, many computer scientists, meta-researchers, librarians, and start-ups were shocked, because they had been building an ecosystem of information services around the database.

Microsoft Academic is not the company’s first attempt to build a literature search tool. An earlier project, Microsoft Academic Search, ran from 2009 to 2012 and fell into shabby disrepair before being officially relaunched as Microsoft Academic in 2016. This is indicative of how Microsoft had never intended to enter into the business of scholarly metadata. Instead, the tech giant has been using data on scholarly communication as testing ground for big data and artificial intelligence (AI) technologies, as a recent article by Redmond researchers suggests. It is rumoured that Microsoft may offer the tested technologies to harvest knowledge from documents in Office 365.

While traditional citation indexes, such as Web of Science and Scopus, are mainly based on selected journals, Microsoft Academic’s strength has been the way it crawls the web and it’s use of AI technologies to populate its database. It is thus not surprising that Microsoft Academic has been faster at indexing new publications and contains significantly more records (194 million, without patents) than the Web of Science Core Collection (79 million) and Scopus (75 million). Microsoft Academic also encompasses a much broader range of publication types (preprints, working papers, dissertations, etc.) and shines in research fields that traditional citation databases often do not cover well, such as computer science, social sciences, and humanities.

A major advantage of Microsoft Academic over Google Scholar is the search interface, which for now still offers ample filtering and sorting options and provides various rankings (topics, journals, institutions, etc.) as well as visualizations of summary statistics. Although the search engine is free of charge and features an integrated social network for academics, it has never been popular with researchers, as can be seen from web traffic statistics:

Total visits in April 2021 according to SimilarWeb (in million)






The main reason for this low usage is likely the search interface itself. It differs fundamentally from traditional academic search systems as it is driven by AI technologies. Specifically, the interface offers true semantic search instead of the usual keyword search with Boolean operators. Or as Microsoft once explained: «Microsoft Academic understands the meaning of words, it doesn’t just match keywords to content. For example, when you type «Microsoft», it knows you mean the institution, and shows you publications authored by researchers affiliated with Microsoft». In addition, the search engine is based on more than 700,000 «fields of study» (i.e., topics or concepts) that were created and are continuously expanded by algorithms, whereas other search systems use fixed, human curated, and less complex classifications. Furthermore, the search engine employs two unique metrics, saliency and estimated citation counts, which are difficult to understand and interpret for most users. Overall, these AI-driven features create a search experience that is very different from what users are accustomed to. It thus seems that the AI technologies employed are either too avant-garde for users or not mature enough.


The Microsoft Academic search engine will operate until December 31, 2021, so we suggest you take advantage of its capabilities.

We would also like to note that in Microsoft Academic you can see a selection of articles from the following our journals:

– Eastern-European Journal of Enterprise Technologies;

– Technology audit and production reserves


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