This paper proposes a novel approach to semantic ontology alignment using contextual descriptors. A formalization was developed that enables the integration of essential and contextual descriptors to create a comprehensive knowledge model. The hierarchical structure of the semantic approach and the mathematical apparatus for analyzing potential conflicts between concepts, particularly in the example of "Transparency" and "Privacy" in the context of artificial intelligence, are demonstrated. Experimental studies showed a significant improvement in ontology alignment metrics after the implementation of contextual descriptors, especially in the areas of privacy, responsibility, and freedom & autonomy. The application of contextual descriptors achieved an average overall improvement of approximately 4.36%. The results indicate the effectiveness of the proposed approach for more accurately reflecting the complexity of knowledge and its contextual dependence.

翻译：本文提出了一种利用上下文描述符进行语义本体对齐的新方法。通过形式化建模，实现了本质描述符与上下文描述符的集成，从而构建了综合知识模型。研究展示了语义方法的层次结构，以及用于分析概念间潜在冲突的数学工具——特别是在人工智能语境下以“透明度”与“隐私性”为例的冲突分析。实验研究表明，引入上下文描述符后，本体对齐的各项指标均得到显著提升，尤其在隐私性、责任性以及自由与自主性等领域表现突出。上下文描述符的应用使整体性能平均提升约4.36%。结果表明，所提方法能更精确地反映知识的复杂性及其上下文依赖性，具有显著的有效性。