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The paper describes a dynamic framework for the construction and maintenance of an expert-finding repository through the continuous gathering and processing of online information. An initial set of online sources, relevant to the topic of interest, is identified to perform an initial collection of author profiles and publications. The extracted information is used as a seed to further enrich the expert profiles by considering other, potentially complementary, online data sources. The resulting expert repository is represented as a graph, where related author profiles are dynamically clustered together via a complex author disambiguation process leading to continuous merging and splitting of author nodes. Several rules are developed that assign weights to the links in the graph based on author similarities such as name, affiliation, e-mail, co-authors, and interests. Dynamic clustering of the authors depending on these weights results in the identification of unique experts for a specific domain. The developed disambiguation and author clustering algorithms are validated on several authors with varying name notations showing an improvement on the identification of unique profiles of 28% compared to the

A Graph-based Disambiguation Approach for Construction of an Expert Repository from Public Online Sources