概率论知识图谱构建的思考与实践
摘 要：概率论是统计学、人工智能（AI）等学科的基础，由于传统的知识载体多以线性文本形式呈现，知识之间的关联关系被隐藏，因而无法直观地展现知识体系的全貌。为满足AI等领域的研究者学习与应用概率论体系结构的需求，其知识图谱的构建具有重要的现实意义。考虑到当前概率论知识图谱的构建，还没有深度涉及知识逻辑关联的挖掘问题，本文正是基于当前的现状，通过分析概率论知识体系的结构特征，提出一套完整的知识图谱构建算法。该算法涵盖知识抽取、知识表示、知识融合与存储等关键步骤，并结合具体示例验证方法的可操作性和有效性。在算法构建研究的基础上，探讨概率论知识图谱在自主学习、智能教学等领域的应用策略，为相关领域的知识工程实践提供理论参考与技术支撑。
关键词：概率论；知识图谱；知识表示；知识融合
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Thinking and Practice on the Construction of Probability Theory Knowledge Graph
Abstract: Probability theory serves as the fundamental cornerstone of statistics, artificial intelligence and other disciplines. Traditional probability knowledge is mostly presented in linear textual form, which conceals the internal logical connections between knowledge points and fails to intuitively demonstrate the overall framework of the knowledge system. Constructing a probability theory knowledge graph is of great practical significance to support researchers in AI and other fields in learning and applying probabilistic knowledge systematically. Given that existing probability theory knowledge graphs lack in-depth exploration of logical relationship mining between knowledge points, this paper proposes a complete construction algorithm for probability theory knowledge graphs by analyzing the structural characteristics of probability knowledge systems. The algorithm covers the core processes of knowledge extraction, knowledge representation, knowledge integration and knowledge storage, and its feasibility and effectiveness are verified through practical case analysis. On this basis, this paper further explores the application strategies of the probability theory knowledge graph in autonomous learning and intelligent teaching, aiming to provide theoretical guidance and technical support for knowledge engineering research and practice in related fields.
Keywords: Probability Theory; Knowledge Graph; Knowledge Representation; Knowledge Fusion
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