# Knowledge graph revision in the context of unknown knowledge

## TL;DR

This paper explores how to update knowledge graphs when initial knowledge is unknown, using Dalal revision operators and proposing two algorithms for this task.

## Contribution

The paper introduces two algorithms, Flaccid_search and Tight_search, for updating knowledge graphs without prior knowledge.

## Key findings

- Updating knowledge graphs without prior knowledge is a strongly NP-complete problem.
- Flaccid_search and Tight_search algorithms can find desired results under different conditions.
- Both algorithms are proven to be effective in the context of unknown knowledge.

## 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.