Microsoft Academic was a comprehensive research platform designed to assist scientists and researchers in their academic endeavors by leveraging advanced machine learning and cognitive technologies. Here is an overview of what the product did and its key features, although it is important to note that the service was retired on December 31, 2021.
Purpose and Functionality
Microsoft Academic was an open discovery service aimed at facilitating the search, analysis, and utilization of scholarly scientific works. It used AI-powered machine readers to process and extract scholarly entities and their relationships from a vast array of documents, including articles, conference papers, patents, theses, and more. This information was organized into the
Microsoft Academic Graph (MAG), a heterogeneous graph that represented entities and their relationships in scholarly communications.
Key Features
Advanced Search Capabilities
The platform offered advanced search features that allowed users to refine their queries based on various criteria such as publication date, author affiliation, journal name, and citation count. Users could also sort results by relevance or date to ensure they accessed the most up-to-date information.
Semantic Search and Recommendations
Microsoft Academic employed semantic inference to recognize query intent and retrieve the most relevant knowledge from the MAG. It acted as a personal assistant by recommending materials that users might not know exist and alerting them to recent publications and breaking news in their field of interest.
Integration with Microsoft Office Suite
The service seamlessly integrated with other tools within the Microsoft Office Suite, enabling researchers to easily import citations into documents, create bibliographies, and manage references using software like Word or 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 platform used AI to extract and organize knowledge from scholarly documents. It deployed machine readers to process documents discovered by Bing crawlers and extract entities and their relationships, which were then used to form a comprehensive knowledge base.
Importance Assessment and Ranking
Microsoft Academic used reinforcement learning algorithms to estimate and quantify the importance of each entity based on future citations. This helped in reasoning and inferences, allowing the platform to predict community judgments effectively.
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 applications, including analytics, search, and recommendation scenarios.
In summary, Microsoft Academic was a powerful tool for researchers, offering advanced search capabilities, semantic search and recommendations, integration with Microsoft Office tools, and robust data access through APIs. Although the service is no longer available, its features and functionality set a high standard for academic research platforms.
