Feature extraction for the author name disambiguation problem in a bibliographic database

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Silva, JMB (Autor) Outra; Silva, F (Autor) FCUP

Ata de Conferência Internacional

Título: Proceedings of the Symposium on Applied Computing, SAC 2017, Marrakech, Morocco, April 3-7, 2017

Páginas: 783-789

Indexação

Scopus - 12 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-00M-X02

DOI: 10.1145/3019612.3019663

Abstract (EN): Author name disambiguation in bibliographic databases has been, and still is, a challenging research task due to the high uncertainty there is when matching a publication author with a concrete researcher. Common approaches normally either resort to clustering to group author's publications, or use a binary classifier to decide whether a given publication is written by a specific author. Both approaches benefit from authors publishing similar works (e.g. subject areas and venues), from the previous publication history of an author (the higher, the better), and validated publicationauthor associations for model creation. However, whenever such an algorithm is confronted with different works from an author, or an author without publication history, often it makes wrong identifications. In this paper, we describe a feature extraction method that aims to avoid the previous problems. Instead of generally characterizing an author, it selectively uses features that associate the author to a certain publication. We build a Random Forest model to assess the quality of our set of features. Its goal is to predict whether a given author is the true author of a certain publication. We use a bibliographic database named Authenticus with more than 250, 000 validated author-publication associations to test model quality. Our model achieved a top result of 95.37% accuracy in predicting matches and 91.92% in a real test scenario. Furthermore, in the last case the model was able to correctly predict 61.86% of the cases where authors had no previous publication history. Copyright 2017 ACM.

Idioma: Inglês

Tipo (Avaliação Docente): Científica