Microsoft Academic was a comprehensive research tool developed by Microsoft to assist scientists and researchers in their academic endeavors. Although the service was retired on December 31, 2021, here is an overview of what it offered during its operational period:
Purpose and Functionality
Microsoft Academic was designed to leverage machine learning and cognitive capabilities to help researchers find, organize, and utilize scholarly content more efficiently. It was built on the Microsoft Academic Graph (MAG), a vast knowledge base that included scholarly entities and their relationships, such as papers, authors, institutions, and subject fields.
Key Features
Advanced Search and Recommendation
The platform featured advanced search capabilities that allowed users to refine their queries using various filters, including publication date, author affiliation, journal name, and citation count. It also provided semantic query suggestions and recommendations based on the user’s search intent, helping researchers discover relevant materials they might not have known existed.
Integration with Microsoft Office Suite
Microsoft Academic seamlessly integrated with the Microsoft Office Suite, enabling researchers to easily import citations into documents, create bibliographies, and manage references using tools like Word and PowerPoint. This integration streamlined the process of writing and publishing academic papers, reducing the need for manual entry of references and ensuring accuracy and consistency.
Knowledge Acquisition and Reasoning
The service used AI-powered machine readers to process documents discovered by Bing crawlers, extracting scholarly entities and their relationships to form the MAG. This knowledge base was updated biweekly and was accessible through the Microsoft Academic Graph (MAG) and its associated APIs.
Importance Assessment and Ranking
Microsoft Academic employed reinforcement learning algorithms to estimate and quantify the importance of each entity within the knowledge base. This was done by predicting community judgments based on future citations, which served as a delayed reward function.
Data Access and APIs
The underlying MAG data was available for download or access via the Academic Knowledge API. This allowed researchers and developers to utilize the data for various purposes, including self-hosting the API for more extensive use beyond the free-tier limits.
Additional Capabilities
- Real-Time Intent Recognition: The platform used a knowledge-driven, semantic inference-based search and recommendation framework to recognize user intent in real-time and serve relevant knowledge.
- Scalable Discovery: Leveraging Bing’s web crawling infrastructure, Microsoft Academic could discover and index new academic papers efficiently, ensuring a continuously updated database.
- Customizable Search Results: Users could filter search results by various criteria, including date range, author, affiliation, field of study, journal, and conference, and choose to include or exclude news items.
Overall, Microsoft Academic was a powerful tool designed to enhance the research process by providing advanced search features, seamless integration with other Microsoft tools, and a robust knowledge base powered by AI and machine learning.