Abstract
Nowadays, the earthquake has become a very serious topic. The earthquake-related information always appears first in social media. Constructing an earthquake knowledge graph can help dealing with earthquake news social media text data. This paper proposed the BiLSTM-CRF (Bi-directional Long Short Term Memory-Conditional Random Field) model to construct earthquake knowledge graph using news text data. The BiLSTM-CRF model identified the entities and then writes entities and their types to the table so that the entities and relationships between entities can be extracted for earthquake. The entities and relationships between entities have been combined with the form of RDF (Resource Description Framework) to construct earthquake knowledge graph on Neo4j database.
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