用预训练文本嵌入提升属性图的语义分析效率
Applying Text Embedding Models for Efficient Analysis in Labeled Property Graphs
- 将文本嵌入模型融入图分析流程，不改动原有结构
- 节点分类与关系预测准确率显著提升
- 适合需要理解文本属性的图数据分析场景
标签属性图常包含丰富的文本属性，若合理利用可增强分析任务效果。本文探索使用预训练文本嵌入模型，实现此类图的高效语义分析。通过嵌入节点和边的文本属性，支持下游任务如节点分类与关系预测，提升上下文理解能力。该方法将语言模型嵌入无缝集成至图处理流程，无需改变原有结构，证明文本语义能显著提高属性图分析的准确率与可解释性。
原文摘要 · Abstract (English)
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.
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