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Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs

Xinyu Lin, Tianyu Zhang, Chengbin Hou, Jinbao Wang, Jianye Xue, Hairong Lv

TL;DR

The paper tackles the problem of noisy and incomplete semantic information in knowledge graphs for node importance estimation (NIE). It introduces LENIE, a framework that uses clustering-based triplet sampling from KGs to capture diverse context, and node-specific adaptive prompts to guide Large Language Models in generating enriched, accurate augmented node descriptions. These augmented descriptions are encoded into semantic embeddings that initialize and enhance downstream GNN-based NIE models, yielding state-of-the-art performance across three real-world KGs and multiple metrics. The work demonstrates that integrating LLM-based semantic augmentation with KG structure can substantially improve NIE, especially for datasets with sparse semantic descriptions, and provides a publicly available implementation.

Abstract

Node Importance Estimation (NIE) is a task that quantifies the importance of node in a graph. Recent research has investigated to exploit various information from Knowledge Graphs (KGs) to estimate node importance scores. However, the semantic information in KGs could be insufficient, missing, and inaccurate, which would limit the performance of existing NIE models. To address these issues, we leverage Large Language Models (LLMs) for semantic augmentation thanks to the LLMs' extra knowledge and ability of integrating knowledge from both LLMs and KGs. To this end, we propose the LLMs Empowered Node Importance Estimation (LENIE) method to enhance the semantic information in KGs for better supporting NIE tasks. To our best knowledge, this is the first work incorporating LLMs into NIE. Specifically, LENIE employs a novel clustering-based triplet sampling strategy to extract diverse knowledge of a node sampled from the given KG. After that, LENIE adopts the node-specific adaptive prompts to integrate the sampled triplets and the original node descriptions, which are then fed into LLMs for generating richer and more precise augmented node descriptions. These augmented descriptions finally initialize node embeddings for boosting the downstream NIE model performance. Extensive experiments demonstrate LENIE's effectiveness in addressing semantic deficiencies in KGs, enabling more informative semantic augmentation and enhancing existing NIE models to achieve the state-of-the-art performance. The source code of LENIE is freely available at \url{https://github.com/XinyuLin-FZ/LENIE}.

Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs

TL;DR

The paper tackles the problem of noisy and incomplete semantic information in knowledge graphs for node importance estimation (NIE). It introduces LENIE, a framework that uses clustering-based triplet sampling from KGs to capture diverse context, and node-specific adaptive prompts to guide Large Language Models in generating enriched, accurate augmented node descriptions. These augmented descriptions are encoded into semantic embeddings that initialize and enhance downstream GNN-based NIE models, yielding state-of-the-art performance across three real-world KGs and multiple metrics. The work demonstrates that integrating LLM-based semantic augmentation with KG structure can substantially improve NIE, especially for datasets with sparse semantic descriptions, and provides a publicly available implementation.

Abstract

Node Importance Estimation (NIE) is a task that quantifies the importance of node in a graph. Recent research has investigated to exploit various information from Knowledge Graphs (KGs) to estimate node importance scores. However, the semantic information in KGs could be insufficient, missing, and inaccurate, which would limit the performance of existing NIE models. To address these issues, we leverage Large Language Models (LLMs) for semantic augmentation thanks to the LLMs' extra knowledge and ability of integrating knowledge from both LLMs and KGs. To this end, we propose the LLMs Empowered Node Importance Estimation (LENIE) method to enhance the semantic information in KGs for better supporting NIE tasks. To our best knowledge, this is the first work incorporating LLMs into NIE. Specifically, LENIE employs a novel clustering-based triplet sampling strategy to extract diverse knowledge of a node sampled from the given KG. After that, LENIE adopts the node-specific adaptive prompts to integrate the sampled triplets and the original node descriptions, which are then fed into LLMs for generating richer and more precise augmented node descriptions. These augmented descriptions finally initialize node embeddings for boosting the downstream NIE model performance. Extensive experiments demonstrate LENIE's effectiveness in addressing semantic deficiencies in KGs, enabling more informative semantic augmentation and enhancing existing NIE models to achieve the state-of-the-art performance. The source code of LENIE is freely available at \url{https://github.com/XinyuLin-FZ/LENIE}.

Paper Structure

This paper contains 23 sections, 11 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Node Importance Estimation
  4. Large Language Models for Knowledge Graphs
  5. Methodology
  6. Problem Formulation
  7. Overview
  8. Semantic Information Extraction from KG
  9. Augmented Node Descriptions Generation
  10. Downstream GNN-based NIE
  11. Experimental Settings
  12. Dataset
  13. Baseline Methods
  14. Evaluation Metrics
  15. Implementation Details
  16. ...and 8 more sections

Figures (4)

  • Figure 1: A small movie KG with different types of edges (color-coded) and nodes. Popularity indicates the importance score for movie nodes, each with a description, but the description (marked in red) may be insufficient, missing, or inaccurate.
  • Figure 2: The overview of the proposed framework. LENIE extracts diverse semantic information from the given KG, generates augmented descriptions using LLMs, and encodes them into semantic embeddings to enhance downstream NIE performance.
  • Figure 3: Comparison of triplet texts extracted by the clustering-based and random-based strategies. The clustering-based strategy extracts more comprehensive semantic information than the random-based strategy.
  • Figure 4: A case study of LENIE's semantic augmentation in three scenarios. Above the dashed line is the semantic information extracted by LENIE from KGs, and below is the augmented description generated by LLMs given the semantic information.