SocialSearch: enhancing entity search with social network matching

This paper introduces the problem of matching people names to their corresponding social network identities such as their Twitter accounts. Existing tools for this purpose build upon naive textual matching and inevitably suffer low precision, due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage “relational” evidences extracted from the Web corpus. In particular, as such an example, we adopt Web document co-occurrences, which can be interpreted as an “implicit” counterpart of Twitter follower relationships. Using both textual and relational features, 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 ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we...

• Type: Journal
• Year: 2011
• Where: EDBT
• Authors: Gae-won You, Seung-won Hwang, Zaiqing Nie, Ji-Rong Wen
• Keywords: Database, EDBT 2011, Network Graphs, Relevance Score, Web Corpus