Congratulations to Zhang Yilin on her successful graduation!

Zhang Yilin graduated from Heilongjiang University with a bachelor’s degree. In 2022, she began her master’s degree in professional studies at the School of Computer Engineering and Science, Shanghai University. After joining the research group, she followed the guidance of Professors Zhang Rui, Han Yuexing, and Chen Qiaochuan to study material literature information mining methods. Under the careful guidance of her teachers, she completed the following research:

1. Aiming at the problems of long sequence dependency and complex entity relationships in materials literature, a semantically enhanced graph network model is proposed and applied to the literature mining field of composite materials. The model enhances semantic association modelling by constructing a heterogeneous graph and introduces a chunked attention mechanism to efficiently deal with the long sequence problem, effectively overcoming the limitations of traditional models. On this basis, the model uses deep separable convolution to fuse global and local semantic features, and combines the dynamic edge weighting mechanism and deep scoring network to improve the node representation and recognition accuracy, and more effectively captures the semantic relationships of material terms in complex contexts.

2. Aiming at the problems of fuzzy entity boundaries and poor recognition of long entities in general-purpose material texts, a multi-granularity fusion graph network model is proposed to be applied to the task of named entity recognition in the field of material science literature. The model is designed with a new module that fuses multi-granularity semantic features with boundary optimisation strategies: firstly, it enhances the representation ability of semantic features at different scales through gated fusion and cross-granularity interactive attention; secondly, it combines Conditional Random Fields and Contrastive Learning for joint training to synergistically improve the accuracy of boundary recognition and the performance of long entity recognition by taking advantage of their respective strengths.

3. The proposed literature mining method is applied to the prediction of carbon fibre composite material properties and application design. By mining and screening the material experimental literature, nine types of key features closely related to mechanical properties are extracted, and the potential of the literature mining results in performance modelling is experimentally verified. In addition, a material performance prediction system was designed and implemented to support users to upload data files and complete model selection, training and result visualisation, providing an efficient and easy-to-use performance prediction tool for material researchers.

Zhang Yilin joined Alibaba Group after graduation and worked as a software developer. During her postgraduate studies at Shanghai University, she studied hard to enhance her professional knowledge and research ability, and was fortunate to meet many mentors and friends. We hope that Zhang Yilin will not forget her original intention and keep her mission in mind in the future, and that she will overcome the obstacles and move forward.

Essay: Research on Material Literature Mining Methods Based on Semantic Awareness

Code: https://github.com/han-yuexing/2025-thesis-zyl-code