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Knowledge Graph-Based Framework for Decision Making Process with Limited Interaction

Author

Listed:
  • Sivan Albagli-Kim

    (Department of Computer and Information Sciences, Ruppin Academic Center, Emek Hefer 4025000, Israel
    Dror (Imri) Aloni Center for Health Informatics, Ruppin Academic Center, Emek Hefer 4025000, Israel)

  • Dizza Beimel

    (Department of Computer and Information Sciences, Ruppin Academic Center, Emek Hefer 4025000, Israel
    Dror (Imri) Aloni Center for Health Informatics, Ruppin Academic Center, Emek Hefer 4025000, Israel)

Abstract

In this work, we present an algorithmic framework that supports a decision process in which an end user is assisted by a domain expert to solve a problem. In addition, the communication between the end user and the domain expert is characterized by a limited number of questions and answers. The framework we have developed helps the domain expert to pinpoint a small number of questions to the end user to increase the likelihood of their insights being correct. The proposed framework is based on the domain expert’s knowledge and includes an interaction with both the domain expert and the end user. The domain expert’s knowledge is represented by a knowledge graph, and the end user’s information related to the problem is entered into the graph as evidence. This triggers the inference algorithm in the graph, which suggests to the domain expert the next question for the end user. The paper presents a detailed proposed framework in a medical diagnostic domain; however, it can be adapted to additional domains with a similar setup. The software framework we have developed makes the decision-making process accessible in an interactive and explainable manner, which includes the use of semantic technology and is, therefore, innovative.

Suggested Citation

  • Sivan Albagli-Kim & Dizza Beimel, 2022. "Knowledge Graph-Based Framework for Decision Making Process with Limited Interaction," Mathematics, MDPI, vol. 10(21), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3981-:d:954044
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    Citations

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    Cited by:

    1. Dizza Beimel & Sivan Albagli-Kim, 2024. "Enhancing Medical Decision Making: A Semantic Technology-Based Framework for Efficient Diagnosis Inference," Mathematics, MDPI, vol. 12(4), pages 1-21, February.

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