Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System

Book chapter


Yu, H. 2020. Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System. in: Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3 New York Springer.
AuthorsYu, H.
EditorsArai, Kohei, Kapoor, Supriya and Bhatia, Rahul
Abstract

With recent viruses across the world affecting millions and millions of people, the self-healthcare information systems show an important role in helping individuals to understand the risks, self-assessment, and self-educating to avoid being affected. In addition, self-healthcare information systems can perform more interactive tasks to effectively assist the treatment process and health condition management. Currently, the technologies used in such kind of systems are mostly based on text crawling from website resources such as text-searching and blog-based crowdsourcing applications. In this research paper, we introduce a novel Artificial Intelligence (AI) framework to support interactive and causality reasoning for a Chatbot application. The Chatbot will interact with the user to provide self-healthcare education and self-assessment (condition prediction). The framework is a combination of Natural Language Processing (NLP) and Knowledge Graph (KG) technologies with added causality and probability (uncertainty) properties to original Description Logic. This novel framework can generate causal knowledge probability neural networks to perform question answering and condition prediction tasks. The experimental results from a prototype showed strong positive feedback. The paper also identified remaining limitations and future research directions.

Keywordscausality analysis ; healthcare ; artificial intelligence
Year2020
Book titleProceedings of the Future Technologies Conference (FTC) 2020, Volume 3
PublisherSpringer
Place of publicationNew York
SeriesAdvances in Intelligent Systems and Computing
ISBN9783030630911
9783030630928
ISSN2194-5357
2194-5365
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-63092-8_3
Web address (URL)http://dx.doi.org/10.1007/978-3-030-63092-8_3
Output statusPublished
Publication dates31 Oct 2020
Publication process dates
Deposited15 Aug 2022
JournalAdvances in Intelligent Systems and Computing
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