A semantic similarity-based social information retrieval model

Social networks include millions upon millions of users that share and access volume of information. Usually, users of social networks specify in their profiles some skills, hobbies, and interests. These profiles are enriched while interacting. None of the existing social network sites allows impersonal search, i.e., search of new contacts that have specified skills or interests. In this paper, we present a new approach to search people in social networks using pseudo-natural language queries. We make two technical contributions: (1) we integrate a generic and semantic modeling of profiles in the search process. (2) We propose a new semantic query-profiles matching function that extract relevant profiles. We have implemented an experimental prototype to validate our approach that shows encouraging results.