Evaluation of the author disambiguation algorithm of AMiner in an academic metasearcher of Computer Science
Abstract
Author disambiguation is a problem of considerable relevance for academic information retrieval systems. A name disambiguation algorithm implemented in AMiner represents one of the approaches based on Machine Learning with the greatest impact in the present. This work presents an evaluation of the name disambiguation algorithm of AMiner for author disambiguation in the context of an academic metasearcher of Computer Science. Experimental results with data generated by the academic metasearcher indicate an average performance similar to the reference. Likewise, experimental results led to identify special cases of author names that present low performance compared with the average. This finding allowed the identification of an apparent association between the low performance of the algorithm in the context of several authors with the same name and with a low number of publications.
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References
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