Semantic-aware expert partitioning

Abstract

In this paper, we present a novel semantic-aware clustering approach for partitioning of experts represented by lists of keywords. A common set of all different keywords is initially formed by pooling all the keywords of all the expert profiles. The semantic distance between each pair of keywords is then calculated and the keywords are partitioned by using a clustering algorithm. Each expert is further represented by a vector of membership degrees of the expert to the different clusters of keywords. The Euclidean distance between each pair of vectors is finally calculated and the experts are clustered by applying a suitable partitioning algorithm.