Dear Tobias Nyumba , I have read several reviews in the education and engineering contexts, and I have appreciated few times Microsoft Academic Search. The common databases are: Scopus and Web of Sciences. I am interested to learn more about Microsoft Academic Search.

Dear Tobias,

https://www.microsoft.com/en-us/research/project/academic/articles/introducing-the-microsoft-academic-knowledge-exploration-service-makes-v2/

Today we are happy to announce the availability of MAKES version 2, a private, self-hosted version of the popular Project Academic Knowledge (PAK) API.  As we hear feedback from our customers, the most requested feature for our free to use service, Project Academic Knowledge (PAK) API, is the ability to increase the threshold for monthly requests.  MAKES API v2 users to self-host PAK API instances on their own Azure subscription, removing usage limitations.  With this release, data schema and implementation are nearly interchangeable between PAK and MAKES.  Porting an existing application using PAK is straightforward; just deploy MAKES V2 to your Azure subscription and update the PAK endpoints in your application to your MAKES V2 deployment.  MAKES V2 is built on a flexible architecture using standard Azure technologies, allowing you to choose the size of your instance(s) and throughput based on your needs.  MAKES V2 subscriptions are aligned with the most recent data from the Microsoft Academic Graph (MAG) and are provisioned at the same cadence as new versions of MAG are provisioned, generally once a week.

Beyond parity with PAK, MAKES V2 allows you to build custom indexes containing only the information you want from MAG, empowering your solution to surface only the data that you require.  And soon, MAKES V2 will allow you to combine the data in MAG with data that you provide, as well as allow you to create custom entity schemas and customize the language grammar powering the interpret API to create truly custom solutions.

What is the Microsoft Academic Knowledge Exploration Service (MAKES)?

MAKES API’s are designed to deliver top-N results from MAG, giving you the ability to create dynamic real-time knowledge applications.  For example, you can create interactive websites like our Microsoft Academic website, real-time analytics applications like VOS Viewer, interactive dashboards in Power BI or federate your existing search capabilities with publications, authors, institutions, journals and conferences in MAG.  For more information, see our introduction documentation.

Aligned with the Microsoft Academic Graph (MAG) and Project Academic Knowledge (PAK)

The MAKES V2 API’s themselves have always closely mirrored our PAK API’s in their interface but up until this point the schemas and implementations have differed slightly.  MAKES V2 brings these two offerings to parity in interface and data schema to make the transition from our free service to our MAKES V2 Azure self-hosted service as seamless as possible.  Going forward, a single tenant of the MAKES API’s will be to maintain parity as well to ensure an easy transition.  In this way, customers of PAK can use MAKES to scale up and out to meet their needs.

Flexible architecture

When designing MAKES V2 we focused on two areas: simplicity and scalability.  MAKES V2 is built on top of proven Azure technologies which make it easy to deploy, maintains and scale.  Using a simple management tool supplied with your subscription, you can deploy a single instance or scale to multiple instances in multiple regions.  In this release we have also added the ability to create and deploy custom indexes when paired with a MAG subscription, allowing you to scale to only the data your applications and users require.

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An example architecture for a MAKES API deployment

Index only what you need and add what you want

MAKES V2 moves beyond the PAK API’s in one important way, the ability to change what is indexed and delivered through the API’s.  With our tutorials as a starting point, it’s easy to create and index custom subgraphs of MAG. The custom index tutorial shows how to generate a subgraph that only contains entities related to a given institution, a common scenario for customers.  Recently, we have partnered with AI2 and others to generate the CORD-19 dataset for COVID-19 research.  What if you could build a powerful semantic search engine over not just those documents, but also over the prior research and fields of study referenced in those publications?  We set out to do just that and will be sharing this with the community soon.

Over the next few months, we will be adding functionality and tutorials to MAKES that will allow you to bring private data into the index.  Many institutions and organizations have libraries of private publications; in the coming months we will be opening the platform and providing tutorials that will show you how to combine your private library data with the MAG graph to create solutions for your organization.

Advanced features and product Road Map

Private data

We will be publishing tutorials and features to MAKES that will allow you to combine MAG data and private data to create a custom index to power MAKES API’s.  As an example, you will be able to combine private data such as a library of publications or patents owned or created by your organization with MAG to surface them from MAKES API calls.

Custom schema

We will be introducing tutorials and features that will allow you to create custom schemas for the data returned from the MAKES V2 API’s.  As an example, research institutions would be able to add a new property on a paper entity to show the amount of funding associated with each publication.  Using private data to populate new properties can power interesting real-time applications to determine ROI on research projects.

Custom grammar

Both PAK and MAKES V2 Interpret API enables semantic interpretation of natural language queries using an SRGS grammar. We’re excited to announce that over the next few months we will be creating tutorials that teach you how to create and use custom grammars that enable a variety of different NLP scenarios.  As an example of modifying the grammar to enhance the interpret experience, you might want to support the ability to use key terms to narrow results.  To do this, a modification to the grammar could be made to recognize the term ‘published after’ , for instance, as constraint to limit results returned from the interpret API to publications created after the year specified in the query.  Here is an interpret query example that would be supported by this grammar change: “AI papers published after 2015”.  Grammars can also be generated to support non-English language queries, specific terms, etc…

Our team is excited about this new offering and we are looking forward sharing with you the new features coming out over the next few months.  As always, please contact us is you have any questions or feature requests.

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I recently worked on a literature review paper and i have used Microsoft Academics its a nice platform but i recommend that you use other platforms as well like for instance Dimensions. Overhere, i must also add in that before starting review paper one should clearly understand the difference between the academic database and academic search engine. I have classified some of these in the question i asked previously. If you need any more assistanc ei would be glad to help you.

Click the following thread:

https://www.researchgate.net/post/What_is_the_difference_between_Academic_Search_Engine_and_Academic_Database?isAnswerFieldFocused=true