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Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System

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

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1290))

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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.

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Notes

  1. 1.

    https://www.kaggle.com/uciml/breast-cancer-wisconsin-data#data.csv

  2. 2.

    https://pypi.org/project/wikipedia/

  3. 3.

    https://github.com/semanticmachinelearning/HealthChatbot

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Author information

Authors and Affiliations

  1. Institution for Research in Applicable Computing, School of Computer Science and Technology, University of Bedfordshire, Luton, UK

    Hong Qing Yu

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  1. Hong Qing Yu
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Correspondence to Hong Qing Yu .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Yu, H.Q. (2021). Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3. FTC 2020. Advances in Intelligent Systems and Computing, vol 1290. Springer, Cham. https://doi.org/10.1007/978-3-030-63092-8_3

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