Semantic-based Expert Search in Textbook Research Archives.
Marco Pavan, Ernesto William De Luca · 2015
Expert finding and the identification of similar professionals are important tasks for many services provided by companies and institutions. Nowadays, the rapid growth of web services and social and professional networks, allowed different kind of users to share personal data and increased the amount of information available. 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 first provide a general perspective on studies on expert finding, similar people identification and social recommender systems, highlighting some critical issues related to information extraction and user profile definition. We then present a first attempt to create an expert search system to support users in Library and Archiving Communities, such as researchers, students, authors, in the field of Textbook Research, in finding other experts to get in contact with or to start a cooperation. This is organized in three phases: first, we collect information about users in order to build structured profiles; then, we build a Community Knowledge Graph (CKG) which defines relationships and weights among terms that occur in the profile sections, emphasizing information shared by users in the entire analyzed community. As third step, we exploit the CKG structure to define similarity values among users based on weights got by their common terms in the CKG, and their distances in the graph. We conjecture that the CKG allows to model users emphasizing new semantic aspects of relationships among profile elements, and helps to improve the similarity computation and the expert search. We present a preliminar experimental evaluation on real users.