Applying Text Embedding Models for Efficient Analysis in Labeled Property Graphs

• URL: http://arxiv.org/abs/2507.10772v1
• Date: Mon, 14 Jul 2025 19:53:56 GMT
• Title: Applying Text Embedding Models for Efficient Analysis in Labeled Property Graphs
• Authors: Michal Podstawski,
• Abstract summary: This work explores the use of pretrained text embedding models to enable efficient semantic analysis in such graphs.<n>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.
• Score: 0.7856362837294112
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Unified Framework for Interactive Visual Graph Matching via Attribute-Structure Synchronization [4.4315876561900165]
  We propose a novel framework for interactive visual graph matching.<n>In the proposed framework, an attribute-structure synchronization method is developed for representing structural and attribute features.<n>With the designed interfaces, the users can also specify a new target graph with desired structural and semantic features.
  arXiv  Detail & Related papers  (2025-07-26T02:47:09Z)
• Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex [0.16385815610837165]
  BiGTex is a novel architecture that tightly integrates GNNs and LLMs through stacked Graph-Text Fusion Units.<n>BiGTex achieves state-of-the-art performance in node classification and generalizes effectively to link prediction.
  arXiv  Detail & Related papers  (2025-04-16T20:25:11Z)
• Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models [3.9489815622117566]
  Learnable Graph Pooling Token (LGPT) enables flexible and efficient graph representation.<n>Our method achieves a 4.13% performance improvement on the GraphQA benchmark without training the large language model.
  arXiv  Detail & Related papers  (2025-01-29T10:35:41Z)
• GUNDAM: Aligning Large Language Models with Graph Understanding [10.080136100700692]
  We introduce the textbfGraph textbfUnderstanding for textbfNatural Language textbfDriven textbfAnalytical textbfModel (model) This model adapts LLMs to better understand and engage with the structure of graph data, enabling them to perform complex reasoning tasks by leveraging the graph's structure itself.
  arXiv  Detail & Related papers  (2024-09-30T07:59:10Z)
• Hypergraph based Understanding for Document Semantic Entity Recognition [65.84258776834524]
  We build a novel hypergraph attention document semantic entity recognition framework, HGA, which uses hypergraph attention to focus on entity boundaries and entity categories at the same time. Our results on FUNSD, CORD, XFUNDIE show that our method can effectively improve the performance of semantic entity recognition tasks.
  arXiv  Detail & Related papers  (2024-07-09T14:35:49Z)
• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models [31.443478448031886]
  RoSE (Relation-oriented Semantic Edge-decomposition) is a novel framework that decomposes the graph structure by analyzing raw text attributes. Our framework significantly enhances node classification performance across various datasets, with improvements of up to 16% on the Wisconsin dataset.
  arXiv  Detail & Related papers  (2024-05-28T20:54:47Z)
• ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization [32.19963543411396]
  This study constructs a large-scale dataset of comprehensive chart-caption pairs and fine-tuning instructions on each chart. We propose an innovative chart summarization method, ChartThinker, which synthesizes deep analysis based on chains of thought. Built upon the curated datasets, our trained model consistently exhibits superior performance in chart summarization tasks.
  arXiv  Detail & Related papers  (2024-03-17T14:49:09Z)
• Conversational Semantic Parsing using Dynamic Context Graphs [68.72121830563906]
  We consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We focus on models which are capable of interactively mapping user utterances into executable logical forms.
  arXiv  Detail & Related papers  (2023-05-04T16:04:41Z)
• Spectral Augmentations for Graph Contrastive Learning [50.149996923976836]
  Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. We propose a set of well-motivated graph transformation operations to provide a bank of candidates when constructing augmentations for a graph contrastive objective.
  arXiv  Detail & Related papers  (2023-02-06T16:26:29Z)
• GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [53.70520466556453]
  We propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph.
  arXiv  Detail & Related papers  (2021-05-06T12:20:41Z)
• GraphOpt: Learning Optimization Models of Graph Formation [72.75384705298303]
  We propose an end-to-end framework that learns an implicit model of graph structure formation and discovers an underlying optimization mechanism. The learned objective can serve as an explanation for the observed graph properties, thereby lending itself to transfer across different graphs within a domain. GraphOpt poses link formation in graphs as a sequential decision-making process and solves it using maximum entropy inverse reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2020-07-07T16:51:39Z)
• Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
  We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
  arXiv  Detail & Related papers  (2020-04-29T14:22:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.