Applying Text Embedding Models for Efficient Analysis in Labeled Property Graphs

TL;DR

This paper investigates integrating pretrained text embedding models into labeled property graphs to improve semantic analysis, enabling more accurate and interpretable node classification and relation prediction without changing graph structure.

Contribution

It introduces a method to embed textual attributes in property graphs using pretrained language models, enhancing analysis capabilities.

Findings

Improved accuracy in node classification tasks.

Enhanced interpretability of graph analysis.

Effective integration of language models without structural changes.

Abstract

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.