Abstract
Expertise modeling has been the subject of extensive research in two main disciplines - Information Retrieval (IR) and Social Network Analysis (SNA). Both IR and SNA techniques build the expertise model through a document-centric approach providing a macro-perspective on the knowledge emerging from large corpus of static documents. With the emergence of the Web of Data, there has been a significant shift from static to evolving documents, characterized by micro-contributions. Thus, the existing macro-perspective is no longer sufficient to track the evolution of both knowledge and expertise. The aim of this research is to provide an all-encompassing, domain-agnostic model for expertise profiling in the context of dynamic, living documents and evolving knowledge bases. Our approach combines: (i) fine-grained provenance, (ii) weighted mappings of Linked Data concepts to expertise profiles, via the application of IR-inspired techniques on micro-contributions, and (iii) collaboration networks - to create and enrich expertise profiles in community-centered environments.
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Ziaimatin, H. (2011). DC Proposal: Capturing Knowledge Evolution and Expertise in Community-Driven Knowledge Curation Platforms. In: Aroyo, L., et al. The Semantic Web – ISWC 2011. ISWC 2011. Lecture Notes in Computer Science, vol 7032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25093-4_32
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DOI: https://doi.org/10.1007/978-3-642-25093-4_32
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