## 3 Medical Data Sources
### Data Strategy
#### 3.2.2 Two-Step Data Fusion Strategy  
Data fusion is a key step in constructing knowledge graphs from multi-data sources. [PERSON] et al. first constructed domain ontology libraries and used rules such as similarity detection and conflict resolution to fuse ontology libraries of multiple domains to form a global ontology library. They targeted the construction of multi-data source knowledge graphs for generic domains and gave a standardized process from data acquisition, data processing, data fusion, and knowledge graph construction, but it could not be directly used in the medical domain [10]. By researching the literature in the medical field, we found that disease is the most critical entity, doctors need to diagnose diseases through symptoms, treatments need to be selected for different diseases, tests need to be performed to accurately determine diseases, etc. Other medical entities need to cross each other through disease entities. We concluded that the disease entity has the characteristic of a \"transportation hub\" among many predefined medical entities. Based on this characteristic, this paper proposed a two-step data fusion strategy for medical data.  
In addition, the target of data fusion is the integration of data and knowledge from different data sources [11]. The goal of data fusion methods used in multi-data source knowledge graphs is only the data entities themselves, ignoring the fusion after forming a triad of data in the form of \"entity1-relationship type-entity2\". In medical data, there are many different types of disease entities, and it is easy to fuse two disease expressions that are similar but have different meanings into one entity for processing. To avoid causing such errors, this paper prioritized the different data sources according to their characteristics in the data source selection strategy. Moreover, in entity alignment, the medical dictionary is used as the entity alignment standard to avoid this wrong operation of fusing different entities into the same concept.  
The commonly used data fusion methods are to process medical entities from different data sources acquired by different methods through the same entity alignment method, which fails to effectively resolve entity conflicts. The two-step data fusion strategy proposed in this paper added the type of data sources (basic data, core data, and supplementary data) as a consideration to ensure the correctness of entity alignment operation when entity conflicts are encountered. As a result of this process, the triplet data \"entity1-relationship type-entity2\" was processed twice. Finally, according to the entity characteristics of \" hub\" in medical data, medical entities from different data sources were fused according to medical relationship type and data source classification (basic data, core data, supplemental data) based on disease entity names to obtain the final triplet data.  
The two-step data fusion strategy first adopted the most authoritative medical dictionary data source as the alignment standard in the entity alignment process, avoiding the wrong operation of fusing similar disease entities into the same entity. Then, according to the entity characteristics of a \"hub\" in the medical field, the medical data from three data sources were fused according to their respective functions, and the medical entities from different data sources were linked into a \"treatment plan\" in the form of knowledge through disease entities. With the abovementioned two characteristics, we not only improved the effectiveness of data fusion in terms of entity fusion but also improved the effectiveness of knowledge fusion in terms of triplet fusion.