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Applying Text Embedding Models for Efficient Analysis in Labeled Property Graphs

Michal Podstawski

TL;DR

The paper addresses exploiting rich textual attributes in labeled property graphs to improve semantic analysis. It introduces a lightweight, model-agnostic pipeline that encodes serialized textual properties with pretrained text embeddings and applies standard classifiers for node-label and relation-prediction tasks without changing the graph structure. Across diverse Neo4j datasets, the approach yields strong accuracy and interpretable predictions, demonstrating the practical value of integrating textual semantics into LPG analytics. It also discusses limitations and outlines future directions, including hybridizing with topology-aware encoders and supporting dynamic graphs to scale in real-world settings.

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.

Applying Text Embedding Models for Efficient Analysis in Labeled Property Graphs

TL;DR

The paper addresses exploiting rich textual attributes in labeled property graphs to improve semantic analysis. It introduces a lightweight, model-agnostic pipeline that encodes serialized textual properties with pretrained text embeddings and applies standard classifiers for node-label and relation-prediction tasks without changing the graph structure. Across diverse Neo4j datasets, the approach yields strong accuracy and interpretable predictions, demonstrating the practical value of integrating textual semantics into LPG analytics. It also discusses limitations and outlines future directions, including hybridizing with topology-aware encoders and supporting dynamic graphs to scale in real-world settings.

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.

Paper Structure

This paper contains 14 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Graph representation learning.
  4. Attributed and text-rich graphs.
  5. Language models for knowledge graphs and graph--text integration.
  6. Positioning of our approach.
  7. Preliminaries
  8. Datasets
  9. Solution
  10. Results
  11. Limitations and Scope
  12. Next Steps
  13. Conclusions
  14. Acknowledgments.

Figures (1)

  • Figure 1: Relation Prediction Task Setting: In this setting, a specific relation of the source node (e.g., between a Player and a Team in the WWC 2019 dataset) is withheld prior to embedding generation. The embedding model encodes the source node based on its remaining relations and neighboring nodes. The resulting embedding is then used in a downstream classification task to predict the correct target node, effectively recovering the withheld relation.