Author name disambiguation in scientific publications faces critical challenges in the era of big data, mainly due to homonymy (different authors with the same name) and synonymy (multiple variants for one author). This study addresses the problem using the Hybrid Framework for Author Name Disambiguation (HFAND), which combines a co-authorship ontology to model semantic relationships with deep neural networks, evaluated on the LAGOS-AND benchmark. Three models (MLP, LSTM, GRU) were implemented using Python3/TensorFlow 2 with optimizations in Rust for scalability. Parallelization in Rust reduced training time versus standard implementations that were not able to run due to the high dimensionality of LAGOS-AND, highlighting computational advantages. The main contribution includes the optimization strategy of AND Ontology adaptable to multidisciplinary domains and a reproducible pipeline that unites the efficiency of Rust with the flexibility of Python for deep learning. These innovations improve scholarly metadata management, with practical applications in automated recommender systems and dynamic researcher profiling. Results show that the MLP achieves an F1-score of 90.51%, outperforming other approaches. As future perspectives, it is proposed to extend the framework to multilingual data and temporal dynamics, essential for global academic repositories with constant updating. This work establishes a methodological advance by demonstrating synergies between semantic representation (ontologies) and neural models, offering scalable solutions for disambiguation problems in complex and exponentially growing academic environments.