  Background: Automatic extraction of chemical-disease relations (CDR) from
unstructured text is of essential importance for disease treatment and drug
development. Meanwhile, biomedical experts have built many highly-structured
knowledge bases (KBs), which contain prior knowledge about chemicals and
diseases. Prior knowledge provides strong support for CDR extraction. How to
make full use of it is worth studying. Results: This paper proposes a novel
model called "Knowledge-guided Convolutional Networks (KCN)" to leverage prior
knowledge for CDR extraction. The proposed model first learns knowledge
representations including entity embeddings and relation embeddings from KBs.
Then, entity embeddings are used to control the propagation of context features
towards a chemical-disease pair with gated convolutions. After that, relation
embeddings are employed to further capture the weighted context features by a
shared attention pooling. Finally, the weighted context features containing
additional knowledge information are used for CDR extraction. Experiments on
the BioCreative V CDR dataset show that the proposed KCN achieves 71.28%
F1-score, which outperforms most of the state-of-the-art systems. Conclusions:
This paper proposes a novel CDR extraction model KCN to make full use of prior
knowledge. Experimental results demonstrate that KCN could effectively
integrate prior knowledge and contexts for the performance improvement.
