Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
SIOC: an approach to connect web-based communities
International Journal of Web Based Communities
Combining RDF Vocabularies for Expert Finding
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Integrating multiple windows and document features for expert finding
Journal of the American Society for Information Science and Technology
Dynamically constructing user profiles with similarity-based online incremental clustering
International Journal of Advanced Intelligence Paradigms
A Study of the Dependencies in Expert Finding
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Understanding the semantics of data provenance to support active conceptual modeling
Active conceptual modeling of learning
Discovering users' topics of interest on twitter: a first look
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Finding co-solvers on twitter, with a little help from linked data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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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 macroperspective is no longer sufficient to track the evolution of both knowledge and expertise. The aim of this research is to provide an all-encompassing, domainagnostic model for expertise profiling in the context of dynamic, living documents and evolving knowledge bases. Our approach combines: (i) finegrained provenance, (ii) weighted mappings of Linked Data concepts to expertise profiles, via the application of IR-inspired techniques on microcontributions, and (iii) collaboration networks - to create and enrich expertise profiles in community-centered environments.