Papers Multi-modal Knowledge Graph
“Multi-modal Knowledge Graph” 태그가 달린 논문 26편 · 필터 해제
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
Domain-specific QA systems require not just generative fluency but high factual accuracy grounded in structured expert knowledge. While recent Retrieval-Augmented Generation (RAG) frameworks improve context recall, they …
graph constructionHallucinationKnowledge GraphsMulti-modal Knowledge Graph+4Multi-modal Knowledge Graph Generation with Semantics-enriched Prompts
Multi-modal Knowledge Graphs (MMKGs) have been widely applied across various domains for knowledge representation. However, the existing MMKGs are significantly fewer than required, and their construction faces numerous …
Graph GenerationKnowledge GraphsMulti-modal Knowledge GraphA Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding
Zero-shot learning (ZL) is crucial for tasks involving unseen categories, such as natural language processing, image classification, and cross-lingual transfer. Current applications often fail to accurately infer and han…
Cross-Lingual TransferGraph Embeddingimage-classificationImage Classification+4VHAKG: A Multi-modal Knowledge Graph Based on Synchronized Multi-view Videos of Daily Activities
Multi-modal knowledge graphs (MMKGs), which ground various non-symbolic data (e.g., images and videos) into symbols, have attracted attention as resources enabling knowledge processing and machine learning across modalit…
BenchmarkingKnowledge GraphsMulti-modal Knowledge GraphMMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph
Multi-modal knowledge graphs have emerged as a powerful approach for information representation, combining data from different modalities such as text, images, and videos. While several such graphs have been constructed …
AttributeContrastive LearningKnowledge GraphsMulti-modal Knowledge Graph+3Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning
Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MM…
Knowledge Graph CompletionKnowledge GraphsMulti-modal Knowledge GraphRelation+1Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples and multi-modal information of the enti…
Contrastive LearningDescriptiveKnowledge Graph CompletionKnowledge Graphs+3VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark
Recently, knowledge editing on large language models (LLMs) has received considerable attention. Compared to this, editing Large Vision-Language Models (LVLMs) faces extra challenges from diverse data modalities and comp…
knowledge editingLanguage ModelingLanguage ModellingMulti-modal Knowledge GraphNoise-powered Multi-modal Knowledge Graph Representation Framework
The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph (MMKG) representation learning framework. Such a framework is essential for embedding structured knowledge into mult…
Entity AlignmentKnowledge Graph CompletionMisconceptionsMulti-modal Entity Alignment+2Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
Multi-modal knowledge graph completion (MMKGC) aims to predict the missing triples in the multi-modal knowledge graphs by incorporating structural, visual, and textual information of entities into the discriminant models…
Knowledge Graph CompletionKnowledge GraphsMulti-modal Knowledge GraphKnowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
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 carefull…
ArticlesEntity Alignmentimage-classificationImage Classification+7Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment
In Multi-Modal Knowledge Graphs (MMKGs), Multi-Modal Entity Alignment (MMEA) is crucial for identifying identical entities across diverse modal attributes. However, semantic inconsistency, mainly due to missing modal att…
AttributeEntity AlignmentGraph LearningKnowledge Graphs+2Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment
Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenges due to the presence of different types…
Entity AlignmentKnowledge GraphsMulti-modal Entity AlignmentMulti-modal Knowledge GraphMACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion
Recent years have seen significant advancements in multi-modal knowledge graph completion (MMKGC). MMKGC enhances knowledge graph completion (KGC) by integrating multi-modal entity information, thereby facilitating the d…
Knowledge Graph CompletionKnowledge GraphsMulti-modal Knowledge GraphAspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities
Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-a…
Extract AspectImage RetrievalKnowledge GraphsMulti-modal Knowledge GraphRECipe: Does a Multi-Modal Recipe Knowledge Graph Fit a Multi-Purpose Recommendation System?
Over the past two decades, recommendation systems (RSs) have used machine learning (ML) solutions to recommend items, e.g., movies, books, and restaurants, to clients of a business or an online platform. Recipe recommend…
BenchmarkingCollaborative FilteringGraph EmbeddingKnowledge Graph Embedding+3Do as I can, not as I get
This paper proposes a model called TMR to mine valuable information from simulated data environments. We intend to complete the submission of this paper.
Knowledge GraphsMulti-modal Knowledge GraphReinforcement Learning (RL)Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding
Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training. However, existing NS methods are unsuitable when mul…
Graph EmbeddingKnowledge Graph EmbeddingMulti-modal Knowledge GraphVision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attributes (including literals), but ignore i…
Entity AlignmentKnowledge GraphsMulti-modal Knowledge GraphMMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning
Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleten…
Knowledge GraphsMissing ElementsMulti-modal Knowledge Graph