Research on an Innovation Opportunity Identification Method Based on Link Prediction in Heterogeneous Networks

A large number of potential knowledge associations in scientific and technological innovation activities have not yet become explicit. How to identify potential valuable innovation opportunity clues from complex knowledge structures has therefore become an important issue in intelligence analysis research. This study takes the field of rice drought-tolerant breeding as an empirical case. Based on PMC full-text literature data, the LightRAG model was employed to extract innovation-related entities and their semantic relationships, including varieties, genes, proteins, phenotypes, and technological methods. A technology-data heterogeneous network for rice drought-tolerant breeding was then constructed, and the HetGNN link prediction method was introduced to predict potential relationships within the network. To evaluate the effectiveness of the proposed model, literature published from 2006 to 2020 was used to construct the training network, while newly emerging relationships extracted from literature published between 2021 and 2023 were used as a future validation set. Adamic-Adar and Node2Vec were further selected as baseline models for comparison. The experimental results show that the proposed method achieved an AUC of 0.8901, an AP of 0.9190, and an F1@0.5 of 0.8322 on the internal testing set, outperforming the baseline models in overall performance. In the temporal holdout validation, the model was able to identify some newly emerging knowledge associations that subsequently appeared in the 2021–2023 literature. The prediction results based on the full dataset indicate that the potential relationships are mainly concentrated in influence relationships between data elements and drought-tolerant phenotypes, as well as support relationships between technological methods and drought-tolerant phenotype research. This study constructs an analytical framework consisting of “innovation element extraction, heterogeneous network modeling, temporal holdout validation, and potential relationship interpretation,” thereby providing a methodological reference for identifying potential innovation opportunity clues from complex scientific knowledge structures.