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Dynamic causality knowledge graph generation for supporting the chatbot healthcare system

  • Hong Qing Yu
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 30-45

Publication milestones

  • Published - 31/10/2020

Publication status

Published - 31/10/2020

Volume

1290

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

ISBN (Print)

9783030630911

ISBN (Electronic)

9783030630928

External Publication IDs

  • handle.net: 10547/624730
  • Scopus: 85096464640

Host publication title

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3

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