World Wide Web DOI 10.1007/s11280-012-0165-5 SocialSearch + : enriching social network with web evidences Gae-won You · Jin-woo Park · Seung-won Hwang · Zaiqing Nie · Ji-Rong Wen Received: 15 July 2011 / Revised: 29 March 2012 / Accepted: 9 April 2012 © Springer Science+Business Media, LLC 2012 Abstract This paper introduces the problem of searching for social network ac- counts, e.g., Twitter accounts, with the rich information available on the Web, e.g., people names, attributes, and relationships to other people. For this purpose, we need to map Twitter accounts with Web entities. However, existing solutions building upon naive textual matching inevitably suffer low precision due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nick- names). To overcome these limitations, we leverage “relational” evidences extracted from the Web corpus. We consider two types of evidence resources—First, web-scale entity relationship graphs, extracted from name co-occurrences crawled from the Web. This co-occurrence relationship can be interpreted as an “implicit” counterpart of Twitter follower relationships. Second, web-scale relational repositories, such as Freebase with complementary strength. Using both textual and relational features obtained from these resources, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance This work builds on and significantly extends our preliminary work [23]. G.-w. You · J.-w. Park · S.-w. Hwang (B ) Pohang University of Science and Technology, Pohang, Republic of Korea e-mail: swhwang@postech.ac.kr G.-w. You e-mail: gwyou@postech.ac.kr J.-w. Park e-mail: jwpark85@postech.ac.kr Z. Nie · J.-R. Wen Microsoft Research Asia, Beijing, People’s Republic of China Z. Nie e-mail: znie@microsoft.com J.-R. Wen e-mail: jrwen@microsoft.com