VAT-KG: Knowledge-Intensive Multimodal Knowledge Graph Dataset for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2506.21556v1
• Date: Wed, 11 Jun 2025 07:22:57 GMT
• Title: VAT-KG: Knowledge-Intensive Multimodal Knowledge Graph Dataset for Retrieval-Augmented Generation
• Authors: Hyeongcheol Park, MinHyuk Jang, Ha Dam Baek, Gyusam Chang, Jiyoung Seo, Jiwan Park, Hogun Park, Sangpil Kim,
• Abstract summary: We propose the first concept-centric and knowledge-intensive multimodal knowledge graph that covers visual, audio, and text information.<n>Our construction pipeline ensures cross-modal knowledge alignment between multimodal data and fine-grained semantics.<n>We introduce a novel multimodal RAG framework that retrieves detailed concept-level knowledge in response to queries from arbitrary modalities.
• Score: 3.1033038923749774
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal Knowledge Graphs (MMKGs), which represent explicit knowledge across multiple modalities, play a pivotal role by complementing the implicit knowledge of Multimodal Large Language Models (MLLMs) and enabling more grounded reasoning via Retrieval Augmented Generation (RAG). However, existing MMKGs are generally limited in scope: they are often constructed by augmenting pre-existing knowledge graphs, which restricts their knowledge, resulting in outdated or incomplete knowledge coverage, and they often support only a narrow range of modalities, such as text and visual information. These limitations reduce their extensibility and applicability to a broad range of multimodal tasks, particularly as the field shifts toward richer modalities such as video and audio in recent MLLMs. Therefore, we propose the Visual-Audio-Text Knowledge Graph (VAT-KG), the first concept-centric and knowledge-intensive multimodal knowledge graph that covers visual, audio, and text information, where each triplet is linked to multimodal data and enriched with detailed descriptions of concepts. Specifically, our construction pipeline ensures cross-modal knowledge alignment between multimodal data and fine-grained semantics through a series of stringent filtering and alignment steps, enabling the automatic generation of MMKGs from any multimodal dataset. We further introduce a novel multimodal RAG framework that retrieves detailed concept-level knowledge in response to queries from arbitrary modalities. Experiments on question answering tasks across various modalities demonstrate the effectiveness of VAT-KG in supporting MLLMs, highlighting its practical value in unifying and leveraging multimodal knowledge.

Related papers

• mKG-RAG: Multimodal Knowledge Graph-Enhanced RAG for Visual Question Answering [29.5761347590239]
  Retrieval-Augmented Generation (RAG) has been proposed to expand internal knowledge of Multimodal Large Language Models (MLLMs)<n>In this paper, we propose a novel multimodal knowledge-augmented generation framework (mKG-RAG) based on multimodal KGs for knowledge-intensive VQA tasks.
  arXiv  Detail & Related papers  (2025-08-07T12:22:50Z)
• MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs [6.165053219836395]
  We propose MMGraphRAG, which refines visual content through scene graphs and constructs a multimodal knowledge graph.<n>It employs spectral clustering to achieve cross-modal entity linking and retrieves context along reasoning paths to guide the generative process.<n> Experimental results show that MMGraphRAG achieves state-of-the-art performance on the DocBench and MMLongBench datasets.
  arXiv  Detail & Related papers  (2025-07-28T13:16:23Z)
• Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts [56.7225771305861]
  This paper introduces Multi-Modal Retrieval-Augmented Generation (M$2$RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models.<n>The benchmark comprises four tasks: image captioning, multi-modal question answering, multi-modal fact verification, and image reranking.<n>To enhance the context utilization capabilities of MLLMs, we also introduce Multi-Modal Retrieval-Augmented Instruction Tuning (MM-RAIT)
  arXiv  Detail & Related papers  (2025-02-24T16:25:25Z)
• Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation [2.549112678136113]
  Retrieval-Augmented Generation (RAG) mitigates issues by integrating external dynamic information for improved factual grounding.<n>Cross-modal alignment and reasoning introduce unique challenges beyond those in unimodal RAG.<n>This survey lays the foundation for developing more capable and reliable AI systems.
  arXiv  Detail & Related papers  (2025-02-12T22:33:41Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Multimodal Reasoning with Multimodal Knowledge Graph [19.899398342533722]
  Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge. We propose the Multimodal Reasoning with Multimodal Knowledge Graph (MR-MKG) method to learn rich and semantic knowledge across modalities.
  arXiv  Detail & Related papers  (2024-06-04T07:13:23Z)
• Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning [51.80447197290866]
  Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning.<n>Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies.<n>We introduce a novel framework with Mixture of Modality Knowledge experts (MoMoK) to learn adaptive multi-modal entity representations.
  arXiv  Detail & Related papers  (2024-05-27T06:36:17Z)
• Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation [51.80447197290866]
  Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs.<n>Existing MMKGC methods usually extract multi-modal features with pre-trained models.<n>We introduce a novel framework MyGO to tokenize, fuse, and augment the fine-grained multi-modal representations of entities.
  arXiv  Detail & Related papers  (2024-04-15T05:40:41Z)
• Noise-powered Multi-modal Knowledge Graph Representation Framework [52.95468915728721]
  The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph representation learning framework.<n>We propose a novel SNAG method that utilizes a Transformer-based architecture equipped with modality-level noise masking.<n>Our approach achieves SOTA performance across a total of ten datasets, demonstrating its versatility.
  arXiv  Detail & Related papers  (2024-03-11T15:48:43Z)
• Generative Multi-Modal Knowledge Retrieval with Large Language Models [75.70313858231833]
  We propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases. We demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
  arXiv  Detail & Related papers  (2024-01-16T08:44:29Z)

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.