Deep Neural Network Structure to Improve Individual Performance based Author Classification

This paper proposed an improved method for author name disambiguation problem, both homonym and synonym. The data prepared is the distance data of each pair of author’s attributes, Levenshtein distance are used. Using Deep Neural Networks, we found large gains on performance. The result shows that level of accuracy is 99.6% with a low number of hidden layer

Authors semantic disambiguation on heterogeneous bibliographic sources

Data ambiguity from various sources remains as a complex problem that affects services provided by digital libraries. From the point of view of integration of information from different sources, the challenge of author ambiguity is one of the most important, and there are numerous methods proposed to deal with this issue using different approaches. They generally work for some scenarios but they have important limitations, specially when dealing with heterogeneous sources. In this work, we review a group of existing methods and then propose a technique that combines some of them, also incorporating a measure of distance using semantic technologies to solve the ambiguity of authors while integrating bibliographic data from various sources. This technique has been successfully tested in disambiguating Ecuadorian authors from both internal sources (institutional repositories) and external digital libraries.Sociedad Argentina de Informática e Investigación Operativa (SADIO