Microsoft Concept Graph:Mining Semantic Concepts for Short Text Understanding  

作　　者：Lei Ji Yujing Wang Botian Shi Dawei Zhang Zhongyuan Wang Jun Yan 

机构地区：[1]Microsoft Research Asia,Haidian District,Beijing 100080,China [2]Institute of Computing Technology,Chinese Academy of Sciences,Haidian District,Beijing 100049,China [3]Beijing Institute of Technology,Haidian District,Beijing 100081,China [4]MIX Labs,Haidian District,Beijing 100080,China [5]Meituan NLP Center,Chaoyang District,Beijing 100020,China [6]AI Lab of Yiducloud Inc.,Huayuan North Road,Haidian District,Beijing 100089,China

出　　处：《Data Intelligence》2019年第3期238-270,共33页数据智能（英文）

摘　　要：Knowlege is important for text-related applications.In this paper,we introduce Microsoft Concept Graph,a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages.Microsoft Concept Graph is built upon Probase,a universal probabilistic taxonomy consisting of instances and concepts mined from the Web.We start by introducing the construction of the knowledge graph through iterative semantic extraction and taxonomy construction procedures,which extract 2.7 million concepts from 1.68 billion Web pages.We then use conceptualization models to represent text in the concept space to empower text-related applications,such as topic search,query recommendation,Web table understanding and Ads relevance.Since the release in 2016,Microsoft Concept Graph has received more than 100,000 pageviews,2 million API calls and 3,000 registered downloads from 50,000 visitors over 64 countries.

关 键 词：Knowledge extraction CONCEPTUALIZATION Text understanding 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]