Knowledge Graph Construction of Near-Homonymy and Near-Synonymy English Words

Abstract

This paper investigates the issue of near-homonymy and near-synonymy in English vocabulary learning and explores the significance and practical value of addressing this problem. Through data analysis, we emphasize the widespread demand for English language acquisition and the importance of the English language. Furthermore, we cite research in the field of education to provide evidence of the positive impact of near-homonymy and near-synonymy on word memorization. The main research work of this paper includes the construction of a graph database and the measurement of near-homonymy and near-synonymy. We describe in detail the workflow of building the graph database, including the definition of nodes and edges, as well as edge filtering methods. Additionally, we introduce the mathematical principles and formulas used to calculate near-homonymy and near-synonymy. The experimental section presents the data source and the process of constructing the graph database, including data preprocessing, similarity calculation, and parameter settings. We establish a graph database using neo4j and showcase the intermediate results of near-homonymy and near-synonymy, as well as visualization results. Finally, we summarize the main findings of this paper and discuss the prospects for applying similarity calculations based on word origins.