Author Classification on Bibliographic Data Using Capsule Networks Architecture

The problem with Author Name Disambiguation is to determine whether the same name in the bibliographic archive refers to the same author or not. Currently, author identification on The Labeled Digital Bibliography and Library Project (DBLP) is triggered by a request for an author who finds his publication mixed with other people's writing. Name ambiguity leads to incorrect identification and attribution of credit to authors. Despite much research in the last decade, the issue of ambiguity of the author's name remains largely unsolved. In this paper, the Capsule Networks (CapsNets) method is proposed to resolve the ambiguity of the author's name. The proposed method obtains the best accuracy in four Name Disambiguation problems including homonyms, synonyms, and non-homonyms synonyms, which is an average of 99% on training and testing data. Likewise, the overall data tested has an accuracy of 99.83% with a low error value. In addition, CapsNets were tested with Performance Measurements including Sensitivity, Precision, and F1-Score. Capsnets can identify authors in DBLP bibliographic data by using a number of attributes such as author name, co-author, venue, title, and year.