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学术知识 APIAcademic Knowledge API

欢迎使用学术知识 API。Welcome to the Academic Knowledge API. 借助此服务,可解释用户查询中的学术意图,并可从 Microsoft Academic Graph (MAG) 中检索到大量信息。With this service, you will be able to interpret user queries for academic intent and retrieve rich information from the Microsoft Academic Graph (MAG). MAG 知识库是涵盖整个网页的一个异构实体图谱,它包含实现学术活动建模的多种实体,例如研究领域、作者、机构、论文、地点和活动。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.

MAG 数据源自必应 Web 索引和必应的内部知识库。The MAG data is mined from the Bing web index as well as an in-house knowledge base from Bing. 由于必应索引持续不断,此 API 将通过必应发现和索引获取网页中的最新信息。As a result of on-going Bing indexing, this API will contain fresh information from the Web following discovery and indexing by Bing. 基于此数据库,学术知识 API 实现了由知识驱动的交互对话,从而将被动搜索与主动建议体验、丰富的研究论文图谱搜索结果以及一组论文和相关实体的属性值直方图分布进行无缝结合。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.

要详细了解 Microsoft Academic Graph,请参阅 https://aka.ms/academicgraphFor more information on the Microsoft Academic Graph, see https://aka.ms/academicgraph.

学术知识 API 已从认知服务预览版迁移至认知服务实验室。The Academic Knowledge API has moved from Cognitive Services Preview to Cognitive Services Labs. 项目的新主页是 https://labs.cognitive.microsoft.com/en-us/project-academic-knowledgeThe new homepage for the project is: https://labs.cognitive.microsoft.com/en-us/project-academic-knowledge. 截至 2018 年 5 月 24 日,现有 API 密钥将继续有效。Your existing API key will continue working until May 24th, 2018. 该日期之后,请生成新的 API 密钥。After this date, please generate a new API key. 请注意,现有密钥过期后,付费预览版将立即失效。Please note that paid preview will no longer be available once your existing key expires. 如果 API 的免费层不满足你的需求,请与我们的团队联系。Please contact our team if the free tier of the API is not sufficient for your purposes.

功能Features

学术知识 API 由 4 个相关的 REST 终结点构成:The Academic Knowledge API consists of four related REST endpoints:

  1. interpret - 解释自然语言用户查询字符串。interpret – Interprets a natural language user query string. 返回带批注的解释,实现可预测用户输入内容的丰富的搜索框自动完成体验。Returns annotated interpretations to enable rich search-box auto-completion experiences that anticipate what the user is typing.
  2. evaluate - 评估查询表达式并返回学术知识实体结果。evaluate – Evaluates a query expression and returns Academic Knowledge entity results.
  3. calchistogram - 计算由查询表达式返回的学术实体属性值的分布直方图,例如按年份分布给定作业的引文。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.

综合使用这些 API 方法,可创建丰富的语义搜索体验。Used together, these API methods allow you to create a rich semantic search experience. 假定具有用户查询字符串,则 interpret 方法提供一个带批注的查询和一个结构化的查询表达式,同时选择性地基于潜在学术数据的语义完成用户查询。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. 例如,如果用户键入字符串“latent s”,则 interpret 方法可提供一组已排名的解释,提示用户可能正在搜索研究领域“latent semantic analysis”(潜在语义分析)、论文“latent structure analysis”(语义结构分析)或其他以“latent s”开始的实体表达式。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. 此信息可用于快速引导用户获得所需搜索结果。This information can be used to quickly guide the user to the desired search results.

evaluate 方法可用于从学术知识库中检索出一组相匹配的论文实体,而 calchistogram 方法可用于计算一组论文实体的属性值分布,这可用于进一步筛选搜索结果。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.

入门Getting Started

如需详细文档,请参阅左侧的副标题。Please see the subtopics at the left for detailed documentation. 请注意,为了提高示例的可读性,REST API 调用包含未经 URL 编码的字符(如空格)。Note that to improve the readability of the examples, the REST API calls contain characters (such as spaces) that have not been URL-encoded. 你的代码需要应用适当的 URL 编码。Your code will need to apply the appropriate URL-encodings.