Research, construction and application of bilingual translation knowledge graph
双语翻译知识图谱的研究构建与应用

摘要

知识图谱作为结构化知识表示的重要技术，在自然语言处理等领域应用广泛。本文系统梳理了该领域的研究现状与发展趋势，针对现有方法在跨语言实体对齐和翻译知识整合上的不足，提出一种基于双语翻译关系建模的BTRM模型。该模型通过整合三个模块的协同工作，提升双语知识图谱的构建质量。实验结果表明：BTRM模型在构建难度、翻译准确性和跨语言应用等方面优于传统知识图谱构建方法，尤其在双语言语义对齐和知识融合方面表现突出。研究不仅为双语翻译知识图谱的构建提供了新的理论框架和技术路径，也为机器翻译、跨语言信息检索等应用提供了更可靠支持。

Abstract

As an important technology for structured knowledge representation, knowledge graphs are widely used in natural language processing and other fields. This paper systematically combs the research status and development trends in this field. Aiming at the shortcomings of existing methods in cross-lingual entity alignment and translation knowledge integration, a BTRM (Bilingual Translation Relationship Modeling) model based on bilingual translation relationship modeling is proposed. The model improves the construction quality of bilingual knowledge graphs through the collaborative work of three integrated modules. Experimental results show that the BTRM model outperforms traditional knowledge graph construction methods in terms of construction difficulty, translation accuracy and cross-lingual applications, especially in bilingual semantic alignment and knowledge fusion. This study not only provides a new theoretical framework and technical path for the construction of bilingual translation knowledge graphs, but also offers more reliable support for applications such as machine translation and cross-lingual information retrieval.