Semantic enrichment for adaptive expert search

Abstract

Expert finding and the identification of similar professionals are important tasks for many services provided by companies and institutions. Most of research works focus on a limited set of users, characterized by the same kind of main activities, e.g., researchers, or exploit external knowledge, such as predefined ontologies. An heterogeneous environment, with possible lack of information, and not well structured data, puts forward new challenges, to address the problem of adapting user profiling and consequently expert search. In this paper, we present a first attempt to create an expert search system to support users (such as researchers, students, authors) in finding experts to get in contact or to start a cooperation with in the field of textbook research. Hereby we semantically enrich user profiles building a Community Knowledge Graph (CKG) which defines relationships among users and related items. Furthermore, we present first experimental results that base on real users.