Abstract. A social networking service can become the basis for the information infrastructure of the future. For that purpose, it is important to extract social networks that reflect actual social networks which users have already had. Providing a simple means for users to register their social relations is also important. We propose a method that combines various approaches to extract social networks. Especially, three kinds of networks are extracted: user-registered Know-link networks; Web-mined Web-link networks; and face-to-face Touch-link networks. This paper describes the combination of social network extraction for an eventparticipant community. Analyses on the extracted social networks are also presented. 1

Finding relevant experts in research is often critical and essential for collaboration. Semantics can refine the level of granularity at which the expertise of various experts can be determined, by explicitly expressing relationships between topics and various subtopics using a taxonomy. Such topic-subtopics relationships allow extrapolation of expertise, based on the notion that expertise in subtopics is also indicative of expertise in a topic itself. Additionally, a taxonomy enables enrichment of researcher Expertise Profiles, based on explicit relationships between the topics of a publication and topic-subtopics relationships in the taxonomy. We describe an approach that uses semantics to find experts, expertise as well as collaboration networks, in a Peer-Review setting, using the implicit coauthorship network of the DBLP bibliography and a taxonomy of Computer Science topics. Various collaboration levels, based on degrees of separation, create the added dimension of presenting potentially unknown experts, also qualified for Program Committee (PC) membership, to the PC Chair(s).