Welcome to the Academic Knowledge API. With this service, you will be able to interpret user queries for academic intent and retrieve rich information from the Microsoft Academic Graph (MAG). The MAG knowledge base is a web-scale heterogeneous entity graph comprised of entities that model scholarly activities: field of study, author, institution, paper, venue, and event.
The MAG data is mined from the Bing web index as well as an in-house knowledge base from Bing. As a result of on-going Bing indexing, this API will contain fresh information from the Web following discovery and indexing by Bing. Based on this dataset, the Academic Knowledge APIs enables a knowledge-driven, interactive dialog that seamlessly combines reactive search with proactive suggestion experiences, rich research paper graph search results, and histogram distributions of the attribute values for a set of papers and related entities.
For more information on the Microsoft Academic Graph, see http://aka.ms/academicgraph.
The Academic Knowledge API consists of four related REST endpoints:
- interpret – Interprets a natural language user query string. Returns annotated interpretations to enable rich search-box auto-completion experiences that anticipate what the user is typing.
- evaluate – Evaluates a query expression and returns Academic Knowledge entity results.
- calchistogram – Calculates a histogram of the distribution of attribute values for the academic entities returned by a query expression, such as the distribution of citations by year for a given author.
- graph search – Searches for a given graph pattern and returns the matched entity results.
Used together, these API methods allow you to create a rich semantic search experience. Given a user query string, the interpret method provides you with an annotated version of the query and a structured query expression, while optionally completing the user’s query based on the semantics of the underlying academic data. For example, if a user types the string latent s, the interpret method can provide a set of ranked interpretations, suggesting that the user might be searching for the field of study latent semantic analysis, the paper latent structure analysis, or other entity expressions starting with latent s. This information can be used to quickly guide the user to the desired search results.
The evaluate method can be used to retrieve a set of matching paper entities from the academic knowledge base, and the calchistogram method can be used to calculate the distribution of attribute values for a set of paper entities which can be used to further filter the search results.
The graph search method has two modes: json and lambda. The json mode can perform graph pattern matching according to the graph patterns specified by a JSON object. The lambda mode can perform server-side computations during graph traversals according to the user-specified lambda expressions.
Please see the subtopics at the left for detailed documentation. Note that to improve the readability of the examples, the REST API calls contain characters (such as spaces) that have not been URL-encoded. Your code will need to apply the appropriate URL-encodings.