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    Dynamic causality knowledge graph generation for supporting the chatbot healthcare system

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    Authors
    Yu, Hong Qing
    Affiliation
    University of Bedfordshire
    Issue Date
    2020-10-31
    Subjects
    chatbot
    knowledge graph
    causality analysis
    natural language processing
    artificial intelligent
    healthcare
    
    Metadata
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    Other Titles
    Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3
    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.
    Citation
    Yu HQ (2020) 'Dynamic causality knowledge graph generation for supporting the chatbot healthcare system', Future Technologies Conference - Online, Springer Science and Business Media Deutschland GmbH.
    Publisher
    Springer Science and Business Media Deutschland GmbH
    URI
    http://hdl.handle.net/10547/624730
    DOI
    10.1007/978-3-030-63092-8_3
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-030-63092-8_3
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9783030630911
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-63092-8_3
    Scopus Count
    Collections
    Computing

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