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Knowledge graph revision in the context of unknown knowledge.

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اقرأ على الانترنت اقرأ أكثر حفظ في قائمتي
  • المؤلفون: Wang S;Wang S; Sun F; Sun F
  • المصدر:
    PloS one [PLoS One] 2024 Jul 05; Vol. 19 (7), pp. e0302490. Date of Electronic Publication: 2024 Jul 05 (Print Publication: 2024).
  • نوع النشر :
    Journal Article
  • اللغة:
    English
  • معلومة اضافية
    • المصدر:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: San Francisco, CA : Public Library of Science
    • الموضوع:
      Algorithms* ; Artificial Intelligence* ; Knowledge*; Software ; Humans
    • نبذة مختصرة :
      The role of knowledge graph encompasses the representation, organization, retrieval, reasoning, and application of knowledge, providing a rich and robust cognitive foundation for artificial intelligence systems and applications. When we learn new things, find out that some old information was wrong, see changes and progress happening, and adopt new technology standards, we need to update knowledge graphs. However, in some environments, the initial knowledge cannot be known. For example, we cannot have access to the full code of a software, even if we purchased it. In such circumstances, is there a way to update a knowledge graph without prior knowledge? In this paper, We are investigating whether there is a method for this situation within the framework of Dalal revision operators. We first proved that finding the optimal solution in this environment is a strongly NP-complete problem. For this purpose, we proposed two algorithms: Flaccid_search and Tight_search, which have different conditions, and we have proved that both algorithms can find the desired results.
      Competing Interests: The authors have declared that no competing interests exist.
      (Copyright: © 2024 Wang, Sun. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
    • References:
      IEEE Trans Neural Netw Learn Syst. 2022 Feb;33(2):494-514. (PMID: 33900922)
    • الموضوع:
      Date Created: 20240705 Date Completed: 20240705 Latest Revision: 20240707
    • الموضوع:
      20250114
    • الرقم المعرف:
      PMC11226096
    • الرقم المعرف:
      10.1371/journal.pone.0302490
    • الرقم المعرف:
      38968205