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Learning Disease Causality Knowledge From the Web of Health Data

Hong Qing Yu, Stephan Reiff-Marganiec

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)18(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.297145

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MLA

Yu, Hong Qing, and Stephan Reiff-Marganiec. "Learning Disease Causality Knowledge From the Web of Health Data." IJSWIS vol.18, no.1 2022: pp.1-19. http://doi.org/10.4018/IJSWIS.297145

APA

Yu, H. Q. & Reiff-Marganiec, S. (2022). Learning Disease Causality Knowledge From the Web of Health Data. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-19. http://doi.org/10.4018/IJSWIS.297145

Chicago

Yu, Hong Qing, and Stephan Reiff-Marganiec. "Learning Disease Causality Knowledge From the Web of Health Data," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-19. http://doi.org/10.4018/IJSWIS.297145

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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. We 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. Besides, 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 together. 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.