Abstract:
This Master thesis explores author name disambiguation in Large extensive bibliographic databases. It tackles the challenge of accurately identifying and distinguishing authors who share an ambiguous name within a vast and diverse dataset. The approach proposes following a 6 phases model involving the use of machine learning and network analysis techniques to improve disambiguation accuracy. Practical considerations for implementing these solutions in large databases are also discussed. This work contributes to more reliable and efficient scholarly information management.
