Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:W6LD32XSrecord.jsonopen to challenge →
read the original abstract
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for innovation. In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm. We begin by defining KGs and MMKGs, then explore their construction progress. Our review includes two primary task categories: KG-aware multi-modal learning tasks, such as Image Classification and Visual Question Answering, and intrinsic MMKG tasks like Multi-modal Knowledge Graph Completion and Entity Alignment, highlighting specific research trajectories. For most of these tasks, we provide definitions, evaluation benchmarks, and additionally outline essential insights for conducting relevant research. Finally, we discuss current challenges and identify emerging trends, such as progress in Large Language Modeling and Multi-modal Pre-training strategies. This survey aims to serve as a comprehensive reference for researchers already involved in or considering delving into KG and multi-modal learning research, offering insights into the evolving landscape of MMKG research and supporting future work.
This paper has not been read by Pith yet.
Forward citations
Cited by 6 Pith papers
-
RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
RADD decouples retrieval and reranking in multi-modal KGC via a relation-aware KGE retriever and conditional discrete denoiser, reporting state-of-the-art results on three benchmarks.
-
When Modalities Remember: Continual Learning for Multimodal Knowledge Graphs
MRCKG combines a multimodal-structural curriculum, cross-modal preservation, and contrastive replay to let multimodal knowledge graphs learn new entities and relations over time without catastrophic forgetting.
-
Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images
Authors build a synthetic data generator and two-stage training pipeline for structured abstractive reasoning on multi-modal relational knowledge images, releasing STAR-64K and showing 3B/7B models outperforming GPT-4o.
-
Multi-Perspective Evidence Synthesis and Reasoning for Unsupervised Multimodal Entity Linking
MSR-MEL synthesizes instance-centric, group-level, lexical, and statistical evidence with LLMs and asymmetric teacher-student GNNs to outperform prior unsupervised methods on multimodal entity linking benchmarks.
-
Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation
The system integrates a Neo4j knowledge graph, four-stage symptom matching with LLM verification, genetic-algorithm-optimized proactive questioning, and multimodal evidence-based visualizations to improve diagnostic t...
-
The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims
MSEA uses a master-slave encoder architecture on patent specifications and claims, enhanced with pointer networks and repetition suppression, to generate better summaries as measured by small ROUGE score gains.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.