Entity Resolution Using Social Graphs for Business Applications

Social network such as Linked In maintains profiles for its members in a semi-structured format. A lot of business applications like ad targeting and content recommendations rely on canonicalization of data elements like companies, titles and schools for enabling fine grained advertising or recommending candidates for job postings. In this paper we explore the issues around resolving company names for hundreds of millions of member positions to known company entities using the social graph. We proposed a machine learning approach leveraging three dimensional feature sets including the social graph, social behavior and various content and demographic features. The experiments showed that our approach achieved high precision at a reasonable coverage and is significantly superior to a baseline content based approach.