Labeled property graphs often contain rich textual attributes that can enhance analytical tasks when properly leveraged. This work explores the use of pretrained text embedding models to enable efficient semantic analysis in such graphs. By embedding textual node and edge properties, we support downstream tasks including node classification and relation prediction with improved contextual understanding. Our approach integrates language model embeddings into the graph pipeline without altering its structure, demonstrating that textual semantics can significantly enhance the accuracy and interpretability of property graph analysis.

翻译：标记属性图通常包含丰富的文本属性，若加以恰当利用，可显著增强分析任务的效果。本研究探索使用预训练文本嵌入模型在此类图中实现高效语义分析。通过对节点与边的文本属性进行嵌入表示，我们以改进的上下文理解支持下游任务，包括节点分类与关系预测。本方法将语言模型嵌入集成至图处理流程中，无需改变图结构，证明了文本语义能够显著提升属性图分析的准确性与可解释性。