Research on Author Name Disambiguation Based on Fusion Features and Semantic Fingerprints

Abstract

Author name disambiguation has been a challenging problem in many applications. In order to promote researches to solve name disambiguation, Aminer launched the Open Academic Data Challenge 2018 jointly with Chinese Association for Artificial Intelligence and China Knowledge Centre for Engineering and Technology. Aminer is a scholar-cantered academic search and mining platform covering more than 200 million papers and more than 100 million scholars in various academic fields. Our team proposed a name disambiguation method based on fusion features and semantic fingerprint technique to participate in the competition. The method identified authors with same names through organization feature and co-author feature at first, and then it solves ambiguity names by way of semantic fingerprints which are 128-bit binary vector generated from textual features of papers by Simhash algorithm. Our method scored 0.609 on the verification set and 0.879 on the test set ranking first in the final submission.