Construction and Application of Knowledge Map of French Corpus Based on Graph Embedding Algorithm

In today's information age, natural language processing and semantic analysis technology are more and more widely used in various fields. As one of the most widely used languages in the world, the construction and application of French corpus is of great significance to the research in linguistics, computer science and other fields. However, there are still some challenges in the construction and application of knowledge map for French corpus. Therefore, this paper aims to combine graph embedding algorithm to construct a knowledge map of French corpus and explore its application in natural language processing and semantic analysis. Finally, the effectiveness of the proposed model is verified by experiments, and empirical analysis is carried out in application scenarios such as semantic search and intelligent question answering. The experimental results show that the knowledge map of French corpus based on graph embedding algorithm has obvious advantages in semantic representation and relationship mining, which provides strong support for natural language processing and semantic analysis.