GEEK: Analyzing Online Communities for Expertise Information

GEEK: Analyzing Online Communities for Expertise Information

Lian Shi (Parque Científico Tecnológico de Gijón Edificio Centros Tecnológicos, Spain), Diego Berrueta (Parque Científico Tecnológico de Gijón Edificio Centros Tecnológicos, Spain), Sergio Fernández (Parque Científico Tecnológico de Gijón Edificio Centros Tecnológicos, Spain), Luis Polo (Parque Científico Tecnológico de Gijón Edificio Centros Tecnológicos, Spain), Iván Mínguez (Parque Científico Tecnológico de Gijón Edificio Centros Tecnológicos, Spain), Emilio Rubiera (Parque Científico Tecnológico de Gijón Edificio Centros Tecnológicos, Spain) and Silvino Fernández (Parque Científico Tecnológico de Gijón Edificio Centros Tecnológicos, Spain)
DOI: 10.4018/978-1-60960-040-2.ch002
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Abstract

Finding experts over the Web using semantics has recently received increased attention, especially its application to enterprise management. This scenario introduces many novel challenges to the Web of Data. Gathering Enterprise Expertise Knowledge (GEEK) is a research project which fosters the adoption of Semantic Web technologies within the enterprise environment. GEEK has produced a prototype that demonstrates how to extract and infer expertise by taking into account people’s participation in various online communities (forums and projects). The reuse and interlinking of existing, well-established vocabularies in the areas of person description (FOAF), Internet communities (SIOC), project description (DOAP) and vocabulary sharing (SKOS) are explored in our framework, as well as a proposal for applying customized rules and other enabling technologies to the expert finding task.
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Expert finders are systems that provide answers to expertise questions, in particular, about individuals with a certain competence. These systems have been explored in series of studies, including Streeter & Lochbaum (1988), Krulwich & Bruckey (1996), and Ackerman & McDonald (1996) as well as the studies in Ackerman et al. (2002). Most of these current systems use modern information retrieval techniques to discover expertise from implicit or secondary electronic resources (Zhang et al., 2007). A person's expertise is presented as terms, which are keyword matched using standard IR techniques. The result is usually an unordered list of related people, or a list ordered according to term frequencies. The limitation of these systems is that it is difficult to measure the people's relative expertise levels in particular areas.

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