Dynamic causality knowledge graph generation for supporting the chatbot healthcare system
AuthorsYu, Hong Qing
AffiliationUniversity of Bedfordshire
natural language processing
MetadataShow full item record
Other TitlesProceedings of the Future Technologies Conference (FTC) 2020, Volume 3
AbstractWith 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.
CitationYu HQ (2020) 'Dynamic causality knowledge graph generation for supporting the chatbot healthcare system', Future Technologies Conference - Online, Springer Science and Business Media Deutschland GmbH.
TypeConference papers, meetings and proceedings