Learning Disease Causality Knowledge From the Web of Health Data
International Journal on Semantic Web and Information Systems
; 18(1):19, 2022.
Article
in English
| Web of Science | ID: covidwho-1979482
ABSTRACT
Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. The authors introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. In addition, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a causal probability description logic framework that combines natural language processing (NLP), causality analysis, and extended knowledge graph (KG) technologies. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
International Journal on Semantic Web and Information Systems
Year:
2022
Document Type:
Article
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