Applying Natural Language Processing to Knowledge Graph Construction: Design and Implementation of a Knowledge Extraction Algorithm      

Abstract With the advent of the information age, building a knowledge base for intelligent platforms has become one of the focuses of attention. As a structured representation method for knowledge, knowledge graph can display various domains of knowledge in the form of graphs, providing important support for intelligent platforms. Based on natural language processing (NLP) and other technologies, this paper designs and implements a knowledge extraction algorithm to automatically extract knowledge and information from various data sources, including text analysis, entity recognition, keyword extraction and other functions. By constructing a knowledge graph, different domains of knowledge are presented in the form of graphs, providing an effective way to manage and utilize this knowledge. This paper introduces the design principles and implementation methods of the knowledge extraction algorithm as well as application examples in the process of constructing a knowledge graph. The experimental results show that the proposed method can effectively extract rich knowledge and information from multiple data sources and construct a semantic-rich knowledge graph, which provides important support for the development of subsequent intelligent platforms.