Abstract: The performance of the existing named entity recognition and relation extraction models would sharply decline due to the lack of a large amount of annotated data for epilepsy-related papers. To solve this issue, a zero-resource named entity recognition and relation extraction model in the epilepsy domain is proposed based on medical data and a pre-training model from similar domains. The performance of the existing unsupervised and semi-supervised models on the epilepsy paper data set isevaluated, and then a domain adversarial network and a relation discriminator are introduced based on the characteristics of the data set to effectively improve the construction effect of the epilepsy domain knowledge graph. Electroencephalography (EEG) features of epilepsy patients are embedded into the knowledge graph in a visual modality. While improving the interpretability of EEG analysis, it builds a more intuitive multi-modal knowledge graph.

Key words: knowledge graph, named entity recognition, relation extraction, epilepsy