In today's knowledge-based economy, having the proper expertise is crucial to resolving many tasks. Expertise Finding (EF) is the area of research concerned with matching available experts to given tasks. A standard approach is to input a task description/proposal/paper into an EF system, and receive recommended experts as output. Mostly, EF systems operate either via a content-based approach, which uses the text of the input, as well as the text of the available experts' profiles to determine a match, and structure-based approaches, which use the inherent relationship between the task description and experts, such as is available in citation networks. The majority of methods use one approach (content-based, "C") or the other (structure-based, "S"). The underlying data representation is fundamentally different, which makes combining content-based and structure-based approaches difficult and time consuming. We propose a way to transform the structure-based representation into one usable by content-based approaches, which further lets us integrate both content-based and structure-based analysis methods (C ＋ S).
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