Papers Entity Alignment
“Entity Alignment” 태그가 달린 논문 211편 · 필터 해제
IBMEA: 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 AlignmentBeyond Entity Alignment: Towards Complete Knowledge Graph Alignment via Entity-Relation Synergy
Knowledge Graph Alignment (KGA) aims to integrate knowledge from multiple sources to address the limitations of individual Knowledge Graphs (KGs) in terms of coverage and depth. However, current KGA models fall short in …
Entity AlignmentKnowledge GraphsRelationProgressively Modality Freezing for Multi-Modal Entity Alignment
Multi-Modal Entity Alignment aims to discover identical entities across heterogeneous knowledge graphs. While recent studies have delved into fusion paradigms to represent entities holistically, the elimination of featur…
Entity AlignmentKnowledge GraphsMulti-modal Entity AlignmentUnsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts
Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages, providing users with seamless access to diverse and comprehensive knowledge. Existing methods, mo…
Entity AlignmentKnowledge GraphsRelationEntity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap
Vertical Federated Learning (VFL) is a machine learning paradigm for learning from vertically partitioned data (i.e. features for each input are distributed across multiple "guest" clients and an aggregating "host" serve…
Entity AlignmentEntity ResolutionFederated LearningVertical Federated LearningRelational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective
Foundation Models (FMs) have demonstrated remarkable insights into the relational dynamics of the world, leading to the crucial question: how do these models acquire an understanding of world hybrid relations? Traditiona…
Entity AlignmentRelational ReasoningEntity Alignment with Noisy Annotations from Large Language Models
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. While existing methods heavily rely on human-generated labels, it is prohibitively expensive to incorporate cross-dom…
Active LearningEntity AlignmentKnowledge GraphsEmpowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings
Knowledge-intensive tasks pose a significant challenge for Machine Learning (ML) techniques. Commonly adopted methods, such as Large Language Models (LLMs), often exhibit limitations when applied to such tasks. Neverthel…
Entity AlignmentKnowledge Graph CompletionKnowledge GraphsRepresentation LearningLeveraging Intra-modal and Inter-modal Interaction for Multi-Modal Entity Alignment
Multi-modal entity alignment (MMEA) aims to identify equivalent entity pairs across different multi-modal knowledge graphs (MMKGs). Existing approaches focus on how to better encode and aggregate information from differe…
Contrastive LearningEntity AlignmentKnowledge GraphsMulti-modal Entity AlignmentNALA: an Effective and Interpretable Entity Alignment Method
Entity alignment (EA) aims to find equivalent entities between two Knowledge Graphs. Existing embedding-based EA methods usually encode entities as embeddings, triples as embeddings' constraint and learn to align the emb…
AttributeEntity AlignmentKnowledge GraphsSentenceLambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases
We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type of entity remains unlabeled. To address …
Contrastive LearningEntity AlignmentRepresentation LearningNoise-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+2Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment
Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-modal knowledge graphs for integration. Unfortunately, prior arts have attempted to improve the interaction and fusion of multi-m…
AttributeContrastive LearningEntity AlignmentKnowledge Graphs+2Unlocking the Power of Large Language Models for Entity Alignment
Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their eff…
Code TranslationEntity AlignmentEntity EmbeddingsRepresentation LearningASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects. Recent embedding-based EA methods have achieved state-of-the-art performance in EA yet faced i…
Entity AlignmentGraph Neural NetworkKnowledge GraphsKnowledge 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+7Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
Weakly Supervised Entity Alignment (EA) is the task of identifying equivalent entities across diverse knowledge graphs (KGs) using only a limited number of seed alignments. Despite substantial advances in aggregation-bas…
Entity AlignmentKnowledge GraphsTowards 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+2Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment
Entity alignment, which is a prerequisite for creating a more comprehensive Knowledge Graph (KG), involves pinpointing equivalent entities across disparate KGs. Contemporary methods for entity alignment have predominantl…
Entity AlignmentEntity EmbeddingsKnowledge GraphsLanguage Modelling+1Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs), seeks to identify equivalent entity pairs across these graphs. Most existing approaches regard EA as a graph representation lea…
Entity AlignmentEntity EmbeddingsGraph Representation LearningKnowledge Graphs+3