MMEA: Entity Alignment for Multi-Modal Knowledge Graphs
Entity alignment plays an essential role in the knowledge graph (KG) integration. Though large efforts have been made on exploring the association of relational embeddings between different knowledge graphs, they may fail to effectively describe and integrate the multimodal knowledge in the real application scenario. To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view. Specifically, we first design a novel multi-modal knowledge embedding method to generate the entity representations of relational, visual and numerical knowledge, respectively. Along this line, multiple representations of different types of knowledge will be integrated via a multimodal knowledge fusion module. Extensive experiments on two public datasets clearly demonstrate the effectiveness of the MMEA model with a significant margin compared with the state-of-the-art methods.
Code (1)
Tasks
Entity AlignmentKnowledge GraphsMultimodal Deep LearningMulti-modal Entity AlignmentMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment
The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing wo…
AttributeEntity AlignmentGraph Neural NetworkGraph Representation Learning+4IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment
Multi-modal entity alignment (MMEA) aims to identify equivalent entities between multi-modal knowledge graphs (MMKGs), where the entities can be associated with related images. Most existing studies integrate multi-modal…
Entity AlignmentKnowledge GraphsMulti-modal Entity AlignmentRethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information. However, exist…
BenchmarkingEntity AlignmentKnowledge Graph EmbeddingsKnowledge Graphs+1MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current MMEA algorithms rely on KG-level modali…
Entity AlignmentKnowledge GraphsMulti-modal Entity AlignmentMulti-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 Graph