Portrait of Zaiqing Nie

Zaiqing Nie

Principal Researcher / Research Manager

About

Dr. Zaiqing Nie is a Principal Researcher and leads the Big Data Mining group at Microsoft Research. Nie and his team aim at building Web-Scale Entity Graphs through interactive knowledge mining and crowdsourcing, and they have built several working systems including: Renlifang (Language: Chinese), and Libra Academic Search, and EntityCube.

Before joining Microsoft in April 2004, Zaiqing graduated in May 2004 with a Ph.D. in Computer Science from Arizona State University. He received his Master of Engineering degree in Computer Applications from Tsinghua University in 1998, and his Bachelor of Engineering degree in Computer Science and Technology Tsinghua University from in 1996. His research interests include Data Mining, Big Data, Intelligent Agents, Machine Learning, Web Search, and Crowdsourcing.

Projects

Minder Reader / 读心机器人

Established: June 22, 2016

Minder Reader (中文: 读心机器人) is an artificial intelligence game which leveraging big data mining & crowd-sourcing to build intelligence bot. The bot has been played by over 50 million times. The game itself have been deployed in multiple platforms, including iOS, Android, Windows Phone, Windows 10, and Web. The data mining technology has been transferred into many Microsoft products, such as Bing, Windows, and Cortana. The bot is built on the knowledge mined from Renlifang…

Enterprise Deep Intelligence (EDI)

Established: July 1, 2015

EDI Agent  The EDI agent is a research prototype deployed internally, which can easily schedule meetings for all Microsoft employees. The EDI agent is able to understand natural language requests, find appropriate time slots, book available meeting rooms and track attendee responses. The EDI agent has been very popular since it's released in July 2015. Internal users can talk to the agent through email and Skype for business. EDI Graph The EDI graph is a knowledge…

Microsoft Academic Search

Established: September 1, 2010

By our entity mining and search technologies, we have created the Microsoft Academic Search engine (code name: Libra) to facilitate the exchange of ideas and communications between academic communities. A user entering search queries in Libra can retrieve relevant information on academic papers, scientists, conferences, journals, and interest groups thus generates more accurate, relevant, and efficient results in comparison to document-level ranking.  

EntityCube

Established: February 13, 2009

EntityCube is a research prototype for exploring object-level search technologies, which automatically summarizes the Web for entities (such as people, locations and organizations) with a modest web presence. The Chinese-language version is called Renlifang. The need for collecting and understanding Web information about a real-world entity (such as a person or a product) is mostly collated manually through search engines. However, information about a single entity might appear in thousands of Web pages. Even if a…

Renlifang/EntityCube

Established: July 31, 2008

EntityCube is a research prototype and is a test bed for exploring entity mining and search technologies, which automatically summarizes the Web for entities (such as people, locations and organizations) with a substantial presence. The need for collecting and understanding Web information about a real-world entity (such as a person or a product) is currently fulfilled manually through search engines. However, information about a single entity might appear in thousands of Web pages. Even if…

Publications

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Other

EntityCube / Renlifang (人立方)

EntityCube is a research prototype and is a test bed for exploring entity mining and search technologies, which automatically summarizes the Web for entities (such as people, locations and organizations) with a substantial presence.

The need for collecting and understanding Web information about a real-world entity (such as a person or a product) is currently fulfilled manually through search engines. However, information about a single entity might appear in thousands of Web pages. Even if a search engine could find all the relevant Web pages about an entity, the user would need to sift through all these pages to get a complete view of the entity. EntityCube generates summaries of Web entities from billions of public Web pages that contain information about people, locations, and organizations, and allows for exploration of their relationships. For example, users can use EntityCube to find an automatically generated biography page and social-network graph for a person, and use it to discover a relationship path between two people.

Renlifang is the Chinese version of EntityCube. Renlifang currently has millions of daily page-views during the peak days, and its twenty questions game has become a very popular crowdsourcing game in China for us to collect knowledge about entities and to train our entity mining algorithms through user interaction.

Microsoft Academic Search

By our entity mining and search technologies, we have created the Microsoft academic search engine (code name: Libra) to facilitate the exchange of ideas and communications between academic communities. A user entering search queries in Libra can retrieve relevant information on academic papers, scientists, conferences, journals, and interest groups thus generates more accurate, relevant, and efficient results in comparison to document-level ranking.

Professional Services

 

Work Experiences