Mapping Scientific Disciplines and Author Expertise Based on Personal Bibliography Files
IV '06 Proceedings of the conference on Information Visualization
Building a thailand researcher network based on a bibliographic database
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
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Expert finding is a task of identifying a list of people who are considered experts in a given specific domain. Many previous works have adopted bibliographic records (i.e., publications) as a source of evidence for representing the areas of expertise [1,2]. In this paper, we present an expertise mapping approach based on a probabilistic keyword annotation model constructed from bibliographic data. To build the model, we use the Science Citation Index (SCI) database as the main publication source due to its large coverage on science and technology (S&T) research areas. To represent the expertise keywords, we use the subject category field of the SCI database which provides general concepts for describing knowledge in S&T such as "Biotechnology & Applied Microbiology", "Computer Science, Artificial Intelligence" and "Nanoscience & Nanotechnology". The keyword annotation model contains a set of expertise keywords such that each is represented with a probability distribution over a set of terms appearing in titles and abstracts. Given publication records (perhaps from different sources) of an expert, a set of keywords can be automatically assigned to represent his/her area of expertise.