A Multi-Channel Knowledge-Enhanced Model for Biomedical Relation Extraction

Biomedical relation extraction is crucial for many applications such as biomedical knowledge graph construction and question answering. It is difficult for a typical neural network model to clearly understand the meaning of the complex biomedical text without any knowledge. The knowledge includes external knowledge and inherent prior knowledge within the dataset. The existing methods always integrate the embedded external knowledge obtained through translation-based models like TransE, which is insufficient for a clear understanding of the biomedical entities and relations. In addition, the corpus itself contains abundant prior knowledge that can aid in learning discriminative features. However, this valuable knowledge is not fully utilized in current methods for biomedical relation extraction. In this work, we propose a multi-channel knowledge-enhanced model to extract biomedical relation. It incorporates the external knowledge and the inherent prior knowledge within the dataset into a neural network using multiple channels. On the one hand, the external entity knowledge is deeply exploited by the sequential and structural knowledge channels, respectively. On the other hand, we also explore the prior knowledge in the dataset using an external attention in the prior knowledge channel. The experimental results demonstrate that the proposed model is effective for the biomedical relation extraction.