用上下文描述增强语义对齐，提升隐私等关键概念的匹配精度
Integration of Contextual Descriptors in Ontology Alignment for Enrichment of Semantic Correspondence
- 融合上下文描述构建综合知识模型
- 在隐私等领域平均提升4.36%对齐效果
- 适合关注复杂语义依赖的AI知识工程研究者
本文提出一种基于上下文描述的语义本体对齐新方法。通过形式化框架，整合核心与上下文描述，构建全面的知识模型。展示了语义方法的分层结构及分析概念间潜在冲突的数学工具，以人工智能中的“透明性”与“隐私”为例。实验表明，引入上下文描述后，本体对齐指标显著提升，尤其在隐私、责任、自由与自主领域。整体平均改进约4.36%。结果证明该方法能更准确反映知识的复杂性及其上下文依赖性。
原文摘要 · Abstract (English)
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.
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