Journal
IEEE ACCESS
Volume 9, Issue -, Pages 21094-21101Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3055371
Keywords
Medical diagnostic imaging; Diseases; Knowledge discovery; Knowledge based systems; Data mining; Clustering algorithms; Semantics; Natural language processing; knowledge graph; question and answer system; medical knowledge
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61672138]
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This system integrates medical professional knowledge, knowledge graphs, and question answering systems to meet the high-efficiency question answering needs of existing patients and doctors, providing specific practical value in the medical field.
To meet the high-efficiency question answering needs of existing patients and doctors, this system integrates medical professional knowledge, knowledge graphs, and question answering systems that conduct man-machine dialogue through natural language. This system locates the medical field, uses crawler technology to use vertical medical websites as data sources, and uses diseases as the core entity to construct a knowledge graph containing 44,000 knowledge entities of 7 types and 300,000 entities of 11 kinds. It is stored in the Neo4j graph database, using rule-based matching methods and string-matching algorithms to construct a domain lexicon to classify and query questions. This system has specific practical value in the medical field knowledge graph and question answering system.
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