I-Corps: Automating People Research with Intelligent Analysis and Mining of Social Network Data on the Internet

I-Corps：通过智能分析和挖掘互联网上的社交网络数据实现人员研究自动化

• 批准号: 1264250
• 负责人: Yuanyuan Zhou
• 金额: $ 5万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1264250&HistoricalAwards=false
• 关键词: Corps; Automating; People; Research; Intelligent

The proposed work is to investigate the usefulness and feasibility of previously developed big data analysis on the problem of mining people-related information, differentiating people of the same name, aggregating information from different sources, and inferring people related information such as connections from various data sources. The biggest challenge is entity resolution (sometimes also referred to as entity disambiguation or record linkage), in which the same name may refer to different real world entities. For instances, many or even hundreds of people are named "James Smith". So which data is about the same "James Smith" and can be merged and aggregated together is not an easy question. The proposed solution aims to take on this problem and allow users to easily and quickly get information related to a target person on smartphones or tablets without spending one to two hours to do tedious, error-prone people research.The revolution of Internet has provided a sea of information publicly available. A major part of such information is related to people and their social networks, which are valuable targeted advertisement, sales, marketing, expanding social network, recruiting, job search, etc. Aggregating people-related information is not an easy task. People-information is valuable in various business functions such as recruiting, sales, business development, etc. According to major search engines about one third of search is people search. The proposed work, if successful, could bring people-related information that is currently scattered in many data sources, together without the issue of name ambiguity. This work may also make such information quickly and conveniently to assist business people in networking, making new business connections more effectively and efficiently.

本文拟开展的工作是研究先前开发的大数据分析在挖掘与人相关的信息、区分同名的人、聚合来自不同来源的信息以及推断来自不同数据源的与人相关的信息（如连接）等问题上的有用性和可行性。最大的挑战是实体解析（有时也称为实体消歧或记录链接），其中相同的名称可能指不同的现实世界实体。例如，许多甚至数百人被命名为“詹姆斯·史密斯”。因此，哪些数据是同一个“詹姆斯·史密斯”，可以合并和汇总在一起，并不是一个容易的问题。提出的解决方案旨在解决这一问题，并允许用户在智能手机或平板电脑上轻松快速地获取与目标人物相关的信息，而无需花费一到两个小时进行繁琐且容易出错的人物研究。互联网革命为公众提供了海量的信息。这些信息的很大一部分与人和他们的社交网络有关，这些信息是有价值的定向广告、销售、营销、扩大社交网络、招聘、求职等。汇总与人相关的信息并不是一件容易的事。人事信息在招聘、销售、业务发展等各种业务职能中都很有价值。根据主要的搜索引擎，大约三分之一的搜索是人搜索。如果成功的话，这项工作可以将目前分散在许多数据源中的与人相关的信息整合在一起，而不会产生名称歧义的问题。这项工作还可以使这些信息快速方便地帮助商务人士建立网络，更有效、高效地建立新的业务联系。