A Collective Approach to Scholar Name Disambiguation

This study investigates name disambiguation for scholarly data. We propose a collective approach, which considers the connections of different ambiguous names, such that it initially treats each author reference as a unique author entity and reformulates the bibliography data as a heterogeneous multipartite network. Disambiguation results of one author name propagate to the others in the network. To further deal with the sparsity problem caused by limited available information, we also introduce word-word and venue-venue similarities and measure author similarities by assembling similarities from multiple perspectives. Using three real-life datasets, we experimentally show that our approach is both effective and efficient.