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Abstract：
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.
摘要：
知识图谱的作用包括表示，组织,检索,推理,和知识的应用，为人工智能系统和应用提供丰富而强大的认知基础。当我们学习新事物时，发现一些旧信息是错误的，看到正在发生的变化和进步，并采用新的技术标准，我们需要更新知识图。然而,在某些环境中,最初的知识是无法知道的。例如,我们不能访问软件的完整代码，即使我们买了它。在这种情况下,有没有办法在没有先验知识的情况下更新知识图谱？在本文中，我们正在调查在Dalal修订运算符的框架内是否有解决这种情况的方法。我们首先证明，在这种环境中找到最优解是一个强NP完全问题。为此,我们提出了两种算法：Flaccid_search和Tight_search，有不同的条件，并且我们已经证明了这两种算法都可以找到所需的结果。
