2 research outputs found
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction
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.Comment: Published on BMC Bioinformatics, 16 pages, 5 figure
Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection
Protein-protein interaction (PPI) extraction from published scientific
literature provides additional support for precision medicine efforts.
Meanwhile, knowledge bases (KBs) contain huge amounts of structured information
of protein entities and their relations, which can be encoded in entity and
relation embeddings to help PPI extraction. However, the prior knowledge of
protein-protein pairs must be selectively used so that it is suitable for
different contexts. This paper proposes a Knowledge Selection Model (KSM) to
fuse the selected prior knowledge and context information for PPI extraction.
Firstly, two Transformers encode the context sequence of a protein pair
according to each protein embedding, respectively. Then, the two outputs are
fed to a mutual attention to capture the important context features towards the
protein pair. Next, the context features are used to distill the relation
embedding by a knowledge selector. Finally, the selected relation embedding and
the context features are concatenated for PPI extraction. Experiments on the
BioCreative VI PPI dataset show that KSM achieves a new state-of-the-art
performance (38.08% F1-score) by adding knowledge selection.Comment: Published in Computational Biology and Chemistry; 14 pages, 2 figure