Effective and scalable solutions for mixed and split citation problems in digital libraries

ABSTRACT

In this paper, we consider two important problems that commonly occur in bibliographic digital libraries, which seriously degrade their data qualities: Mixed Citation (MC) problem (i.e., citations of different scholars with their names being homonyms are mixed together) and Split Citation (SC) problem (i.e., citations of the same author appear under different name variants). In particular, we investigate an effective yet scalable solution since citations in such digital libraries tend to be large-scale. After formally defining the problems and accompanying challenges, we present an effective solution that is based on the state-of-the-art sampling-based approximate join algorithm. Our claim is verified through preliminary experimental results.

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