Abstract： In order to solve the problem of data redundancy conflict and lack of association, a knowledge graph visualization fusion method for multi-source heterogeneous data is studied to improve the reliability of data fusion. The domain ontology database and global ontology database corresponding to multi-source heterogeneous data are established by using Web Ontdogy Languge(OWL), so that knowledge entity extraction and knowledge fusion are carried out under the same framework. Based on the Long Short-Term Memory network(LSTM) and Conditional Random Field(CRF) model, knowledge entities conforming to domain definition are extracted from heterogeneous data from multiple sources under the constraint of ontology library. The knowledge fusion model based on hierarchical filtering is used to visualize the extracted knowledge entities, solve the redundant information and inconsistency problems in multi-source heterogeneous data, and form an accurate, complete and reliable multi-source heterogeneous data visualization fusion knowledge graph, which helps to find potential data associations and complete the missing data associations. The experimental results show that with the increase of the proportion of missing data, the scaling coefficient and attribute coverage begin to decrease, and the lowest scaling coefficient and attribute coverage are 0.86 and 0.87, which are significantly higher than the corresponding thresholds. When dealing with four data sources, the visual clarity of the proposed method is 93%~97%, and the information fusion is 92%~96%, which are better than the comparison methods. It shows that the method can effectively extract the knowledge entities of multi-source heterogeneous data, establish the knowledge graph, and realize the visualization fusion of multi-source
Key words : multi-source heterogeneous data；knowledge graph；visual ization fusion；ontology library；long short-term memory network；conditional random field
