Name Disambiguation

Name disambiguation tackles the challenge of identifying distinct individuals with identical or similar names within large datasets, such as scientific publications or patent records. Current research focuses on developing robust algorithms, often employing graph-based approaches, neural networks, and Bayesian methods, to cluster publications or records authored by the same person while distinguishing between different individuals. Improved evaluation frameworks are also a key area of focus, aiming to create more reliable and reusable benchmark datasets. Effective name disambiguation is crucial for accurate data analysis, improved search functionality in digital libraries, and enhanced credit attribution in various fields.