As the importance of the knowledge graph increasing in many fields, finding an efficient way to display and interact with the data is a huge challenge, especially in the medical field. We here revealed an initial proof-of-concept demonstration, a web application. In this demonstration, we will show the user interface to explore or get information from the domain-specific real-world data. The demonstration exhibits the flexibility of displaying complex medical data, helps users to understand and explore their data. We'll have fun with a use case of the demonstration. For more information, please visit our website(http://121.12.85.245:1316/#/ ) or watch the video on YouTube( https://www.youtube.com/watch?v=E4qw-_q5etY).
A knowledge graph represents entities and their relations. In order to help users or domain experts make intelligent decisions and support artificial intelligence systems for medical applications, such as information retrieval, Q&A and so on, we build a huge but high-quality medical knowledge graph in Chinese. Our knowledge graph covers 2.7 million medical terms, including 1 million core terms such as diseases, symptoms, drugs, body structure, examination etc. It also includes various medical relationship triples such as disease-symptoms, diseaseexamination, disease-indications. An entity, such as heart failure(), can be associated with many other entities especially in medical field. One example is that heart failure − has symptom → cyanosis, as same as
Knowledge Graph Entity Linking and Resolution
Knowledge Extraction
Data Processing
Data Collection 2. Data Processing. Cleaning data is the main task in this step. 3. Knowledge Extraction. Information Extraction from the data was handled by last step, include named entity recognition and relation extraction. 4. Entity Linking and Resolution. Based on medical data reviewed by authoritative medical experts, we integrate data from various sources,by applied for entity linking and resolution technology. 5. Knowledge Graph Construction. We store the graph data into the Neo4j database. 6. Knowledge Graph Application. This knowledge graph empowers to many applications in our product. This demonstration shows novel and efficient user interaction of medical data.
There is a lot of work focused on how to visualize knowledge graph [
HTML2. 2
For constructing and using the knowledge graph, we used a dashboard for summarizing the data, as showed in Fig.2. 1 https://neo4j.com/ 2 Chrome browser is recommended A User Interaction Demonstration For Exploring Medical Knowledge Graph
One the second tab, a hierarchical disease tree can be found(Fig.3), which can be folded and unfolded by click.
When we click on the nodes → → in order on the tree, a main view will be showed on the right of page to represent the corresponding data where the center is clicked node. Furthermore, disease, symptom and drug entities associated with it can switch to the center by clicking.
The encyclopedia about the disease which would be showed when the center node being hovered. The content page contains semi-structured information such as overview, diagnosis, treatment, follow-up and common complications.
The main innovation in our demonstration is the dynamic interaction graph displayed on the right column. It consists of many rings, with lots of small dots on it, which can be clicked when the category is disease, drug or symptom(Fig.4). Diferent rings represent diferent node categories, category of the ring can be seen in the legend under the graph. The English mapping can be found on Table 1. When clicking the rings, it will be highlighted and the related node will appear around the center node. The bottom right corner is a track which can go back to the node that was clicked in the previous steps. For more features, please visit our website. disease symptom test bodystruce where disease be found surgery constrained drugs indication drugs susceptible population sub-disease of a disease • heart failure • cyanosis • heart
• radionuclide imaging • endotracheal intubation • aminophylline tablets • chloropropionate tablets • elderly group • chronic heart failure
Only first two disease tree can be clicked in this demonstration. However, the approach to explore and visualize the graph data is novel and efective. We proposed the interface way, showed in the demonstration, would help doctors, domain experts or the public, who are exploring and searching information from a knowledge graph. Meanwhile helping them get access to information and make informed decisions more easily.