Papers Entity Alignment
“Entity Alignment” 태그가 달린 논문 211편 · 필터 해제
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism
Entity alignment (EA) aims to discover the equivalent entity pairs between KGs, which is a crucial step for integrating multi-source KGs.For a long time, most researchers have regarded EA as a pure graph representation l…
Entity AlignmentGraph Representation LearningRepresentation LearningDegree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graph
Entity alignment, as the vital technique for knowledge graph construction and integration, aims to match entities that refer to the same real-world identity in different knowledge graphs (KGs). Recently, much effort has …
Entity AlignmentGenerative Adversarial Networkgraph constructionKnowledge GraphsA Critical Re-evaluation of Neural Methods for Entity Alignment
Neural methods have become the de-facto choice for the vast majority of data analysis tasks, and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA methods have been published since…
Entity AlignmentEntity ResolutionFairnessKnowledge Graph Embedding Methods for Entity Alignment: An Experimental Review
In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various domains, aiming to support applications like question answering, recommendations, etc. A frequent task when integrating knowledge f…
AttributeEntity AlignmentGraph EmbeddingKnowledge Graph Embedding+2Ensemble Semi-supervised Entity Alignment via Cycle-teaching
Entity alignment is to find identical entities in different knowledge graphs. Although embedding-based entity alignment has recently achieved remarkable progress, training data insufficiency remains a critical challenge.…
Entity AlignmentKnowledge GraphsSemi-constraint Optimal Transport for Entity Alignment with Dangling Cases
Entity alignment (EA) merges knowledge graphs (KGs) by identifying the equivalent entities in different graphs, which can effectively enrich knowledge representations of KGs. However, in practice, different KGs often inc…
Entity AlignmentKnowledge GraphsMetric LearningWord EmbeddingsAn Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
Knowledge graph integration typically suffers from the widely existing dangling entities that cannot find alignment cross knowledge graphs (KGs). The dangling entity set is unavailable in most real-world scenarios, and m…
Entity AlignmentKnowledge GraphsDeep Reinforcement Learning for Entity Alignment
Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The most notable is that they identify the …
Decision MakingDeep Reinforcement LearningEntity Alignmentreinforcement-learning+3Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs
Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that contain time information created the need for reasoning over time…
Entity AlignmentKnowledge GraphsSelfKG: Self-Supervised Entity Alignment in Knowledge Graphs
Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its development, the label supervision has bee…
Entity AlignmentKnowledge GraphsSelf-Supervised LearningJointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment
Entity alignment aims at integrating heterogeneous knowledge from different knowledge graphs. Recent studies employ embedding-based methods by first learning the representation of Knowledge Graphs and then performing ent…
BenchmarkingEntity AlignmentEntity EmbeddingsKnowledge Distillation+2Interactive Contrastive Learning for Self-supervised Entity Alignment
Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments. The current SOTA self-supervised EA method draws inspiration from contrastive learni…
Contrastive LearningEntity AlignmentKnowledge GraphsDangling-Aware Entity Alignment with Mixed High-Order Proximities
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some d…
Entity AlignmentKnowledge GraphsVocal Bursts Intensity PredictionTraining Free Graph Neural Networks for Graph Matching
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free). TFGM prov…
Entity AlignmentGraph MatchingKnowledge GraphsInformed Multi-context Entity Alignment
Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources. Previous attempts at entity alignment have explored different KG structures, such as neighborhood-based and path-based conte…
Entity AlignmentEntity EmbeddingsKnowledge GraphsRelationAlign and Prompt: Video-and-Language Pre-training with Entity Prompts
Video-and-language pre-training has shown promising improvements on various downstream tasks. Most previous methods capture cross-modal interactions with a transformer-based multimodal encoder, not fully addressing the m…
cross-modal alignmentEntity AlignmentRetrievalVideo Retrieval+2EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines
Despite encoding enormous amount of rich and valuable data, existing data sources are mostly created independently, being a significant challenge to their integration. Mapping languages, e.g., RML and R2RML, facilitate d…
Entity AlignmentKnowledge GraphsImproving Knowledge Graph Representation Learning by Structure Contextual Pre-training
Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs. In this paper, we propose a novel pre-training-then-fine-tuning f…
Entity AlignmentGraph Representation LearningKnowledge GraphsRepresentation Learning+2Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment
Motivated by the success of pre-trained language models such as BERT in a broad range of natural language processing (NLP) tasks, recent research efforts have been made for adapting these models for different application…
Entity AlignmentDeep Reinforcement Learning for Entity Alignment
Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The most notable is that they identify the …
Decision MakingDeep Reinforcement LearningEntity Alignmentreinforcement-learning+3