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; 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.

拟议的工作是研究有关采矿与人们相关的信息的问题，与众不同的人，从不同来源汇总信息，并推断出与人相关的信息，例如来自各种数据来源的连接。最大的挑战是实体解决方案（有时也称为实体歧义或记录链接），其中同名可能指的是不同的现实世界实体。对于情况，许多甚至数百人被命名为“詹姆斯·史密斯”。因此，哪些数据大于相同的“詹姆斯·史密斯”，可以合并并汇总在一起并不是一个容易的问题。拟议的解决方案旨在解决这个问题，并允许用户轻松，快速获取与智能手机或平板电脑上的目标人有关的信息，而无需花费一到两个小时来进行乏味，容易出错的人进行研究。互联网的革命提供了公开可用的信息。此类信息的主要部分与人们及其社交网络有关，这些信息是有价值的有针对性的广告，销售，市场营销，扩展社交网络，招聘，求职等。汇总与人相关的信息并不是一件容易的事。人们的信息在各种业务职能中都很有价值，例如招聘，销售，业务开发等。根据主要搜索引擎，大约三分之一的搜索是人们搜索。拟议的工作，如果成功的话，可以将目前散布在许多数据源中的与人相关的信息一起，而没有名称歧义的问题。这项工作还可以快速，方便地提供此类信息，以帮助商人建立网络，从而更有效，更有效地进行新的业务联系。