Diverse Knowledge-Embedded Method for Chemical-Induced Disease Relation Extraction

Chemical-induced disease (CID) relation extraction plays an important role for disease treatment, drug repositioning, and drug discovery; thus, researchers employ natural language processing techniques to extract such relationships from the biomedical literature. However, the dispersion of CID relation across various sentences of a document poses a significant challenge for effective extraction. To address this issue, a neural network model is proposed in this paper, and it integrates diverse knowledge and vector cross-self-attention mechanisms for CID relation extraction. The model takes documents, their crucial shortest dependency path trees, and external knowledge as inputs. It utilizes pre-trained language models to generate the basic semantic embedding of all inputs. The proposed vector cross-self-attention is adopted to enhance knowledge representation with the semantics of document. The fusion of document semantics, enhanced shortest dependency path representations, and external knowledge embeddings is used to predict CID relationships. Finally, the proposed model is evaluated on BC5CDR and BioRED datasets. Experimental results demonstrate that diverse knowledge is effective for CID extraction. Chemical–disease pairs derived from both generative LLMs and CTD boost extraction performance. The vector cross-self-attention mechanism effectively enhances knowledge representation by attending to the document representations. This guides the model to identify CID relationships. The proposed model achieves F1-scores of 71.9% and 77.6% on BC5CDR and BioRED, respectively.