From Pixels to Insights: Unsupervised Knowledge Graph Generation with Large Language Model
The role of image data in knowledge extraction and representation has become increasingly significant. This study introduces a novel methodology, termed Image to Graph via Large Language Model (ImgGraph-LLM), which constructs a knowledge graph for each image in a dataset. Unlike existing methods tha...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/5/335 |
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| Summary: | The role of image data in knowledge extraction and representation has become increasingly significant. This study introduces a novel methodology, termed Image to Graph via Large Language Model (ImgGraph-LLM), which constructs a knowledge graph for each image in a dataset. Unlike existing methods that rely on text descriptions or multimodal data to build a comprehensive knowledge graph, our approach focuses solely on unlabeled individual image data, representing a distinct form of unsupervised knowledge graph construction. To tackle the challenge of generating a knowledge graph from individual images in an unsupervised manner, we first design two self-supervised operations to generate training data from unlabeled images. We then propose an iterative fine-tuning process that uses this self-supervised information, enabling the fine-tuned LLM to recognize the triplets needed to construct the knowledge graph. To improve the accuracy of triplet extraction, we introduce filtering strategies that effectively remove low-confidence training data. Finally, experiments on two large-scale real-world datasets demonstrate the superiority of our proposed model. |
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| ISSN: | 2078-2489 |