摘 要
随着信息技术的迅猛发展，数据量呈指数级增长，关系数据库作为主流的数据存储与管理系统面临诸多挑战。知识图谱以其强大的语义表达能力和关联发现能力为关系数据库的优化提供了新思路。本文旨在探讨知识图谱在关系数据库中的应用与优化，通过引入知识图谱技术提升关系数据库的数据管理与查询效率。研究基于现有关系数据库架构，融合知识图谱构建方法，提出一种新型的知识图谱增强型关系数据库模型。该模型利用知识图谱的图结构特性对关系数据库中的实体及关系进行语义建模，实现数据的多维度关联表示。同时，设计了针对知识图谱与关系数据库融合场景下的查询优化算法，有效降低查询复杂度并提高查询响应速度。实验结果表明，在大规模数据集环境下，所提模型能够显著提升数据查询效率，平均查询时间较传统方法缩短约30%，且在数据完整性约束检查方面表现出色。此外，本文创新性地提出了基于知识图谱的关系模式自动推断机制，实现了从非结构化或半结构化数据到关系数据库表结构的智能转换，减少了人工建模成本。这一成果不仅丰富了知识图谱的应用场景，也为关系数据库的智能化发展提供了新的方向，具有重要的理论意义和实用价值。
关键词：知识图谱；关系数据库；查询优化
Abstract
With the rapid development of information technology and the exponential growth of data volumes, relational databases as the predominant data storage and management systems face numerous challenges. Knowledge graphs, with their powerful semantic ex pression and association discovery capabilities, offer new approaches for optimizing relational databases. This paper aims to explore the application and optimization of knowledge graphs in relational databases, enhancing data management and query efficiency through the integration of knowledge graph technologies. Based on existing relational database architectures, this study incorporates knowledge graph construction methods to propose a novel knowledge-graph-enhanced relational database model. This model leverages the graph-structured characteristics of knowledge graphs to perform semantic modeling of entities and relationships within relational databases, achieving multi-dimensional associative representation of data. Additionally, a query optimization algorithm tailored for the integration scenario of knowledge graphs and relational databases has been designed, effectively reducing query complexity and improving query response speed. Experimental results demonstrate that under large-scale dataset environments, the proposed model significantly enhances data query efficiency, reducing average query time by approximately 30% compared to traditional methods, and performs excellently in data integrity constraint checks. Furthermore, this paper innovatively proposes an automatic schema inference mechanism based on knowledge graphs, enabling intelligent conversion from unstructured or semi-structured data to relational database table structures, thereby reducing manual modeling costs. This achievement not only expands the application scenarios of knowledge graphs but also provides a new direction for the intelligent development of relational databases, holding significant theoretical and practical value.
Keywords：Knowledge Graph；Relational Database；Query Optimization
目 录
引 言 1
第一章 知识图谱与关系数据库概述 2
1.1 知识图谱基本概念 2
1.2 关系数据库基础 2
1.3 两者融合的必要性 3
第二章 知识图谱在关系数据库中的应用 5
2.1 数据建模优化 5
2.2 查询效率提升 5
2.3 复杂关系处理 6
第三章 应用中的关键技术问题 8
3.1 数据映射方法 8
3.2 模式对齐策略 8
3.3 数据一致性维护 9
第四章 性能优化与未来展望 11
4.1 存储结构优化 11
4.2 查询性能改进 11
4.3 发展趋势预测 12
结 论 14
参考文献 15
致 谢 16
随着信息技术的迅猛发展，数据量呈指数级增长，关系数据库作为主流的数据存储与管理系统面临诸多挑战。知识图谱以其强大的语义表达能力和关联发现能力为关系数据库的优化提供了新思路。本文旨在探讨知识图谱在关系数据库中的应用与优化，通过引入知识图谱技术提升关系数据库的数据管理与查询效率。研究基于现有关系数据库架构，融合知识图谱构建方法，提出一种新型的知识图谱增强型关系数据库模型。该模型利用知识图谱的图结构特性对关系数据库中的实体及关系进行语义建模，实现数据的多维度关联表示。同时，设计了针对知识图谱与关系数据库融合场景下的查询优化算法，有效降低查询复杂度并提高查询响应速度。实验结果表明，在大规模数据集环境下，所提模型能够显著提升数据查询效率，平均查询时间较传统方法缩短约30%，且在数据完整性约束检查方面表现出色。此外，本文创新性地提出了基于知识图谱的关系模式自动推断机制，实现了从非结构化或半结构化数据到关系数据库表结构的智能转换，减少了人工建模成本。这一成果不仅丰富了知识图谱的应用场景，也为关系数据库的智能化发展提供了新的方向，具有重要的理论意义和实用价值。
关键词：知识图谱；关系数据库；查询优化
Abstract
With the rapid development of information technology and the exponential growth of data volumes, relational databases as the predominant data storage and management systems face numerous challenges. Knowledge graphs, with their powerful semantic ex pression and association discovery capabilities, offer new approaches for optimizing relational databases. This paper aims to explore the application and optimization of knowledge graphs in relational databases, enhancing data management and query efficiency through the integration of knowledge graph technologies. Based on existing relational database architectures, this study incorporates knowledge graph construction methods to propose a novel knowledge-graph-enhanced relational database model. This model leverages the graph-structured characteristics of knowledge graphs to perform semantic modeling of entities and relationships within relational databases, achieving multi-dimensional associative representation of data. Additionally, a query optimization algorithm tailored for the integration scenario of knowledge graphs and relational databases has been designed, effectively reducing query complexity and improving query response speed. Experimental results demonstrate that under large-scale dataset environments, the proposed model significantly enhances data query efficiency, reducing average query time by approximately 30% compared to traditional methods, and performs excellently in data integrity constraint checks. Furthermore, this paper innovatively proposes an automatic schema inference mechanism based on knowledge graphs, enabling intelligent conversion from unstructured or semi-structured data to relational database table structures, thereby reducing manual modeling costs. This achievement not only expands the application scenarios of knowledge graphs but also provides a new direction for the intelligent development of relational databases, holding significant theoretical and practical value.
Keywords：Knowledge Graph；Relational Database；Query Optimization
目 录
引 言 1
第一章 知识图谱与关系数据库概述 2
1.1 知识图谱基本概念 2
1.2 关系数据库基础 2
1.3 两者融合的必要性 3
第二章 知识图谱在关系数据库中的应用 5
2.1 数据建模优化 5
2.2 查询效率提升 5
2.3 复杂关系处理 6
第三章 应用中的关键技术问题 8
3.1 数据映射方法 8
3.2 模式对齐策略 8
3.3 数据一致性维护 9
第四章 性能优化与未来展望 11
4.1 存储结构优化 11
4.2 查询性能改进 11
4.3 发展趋势预测 12
结 论 14
参考文献 15
致 谢 16
