Papers Entity Alignment
“Entity Alignment” 태그가 달린 논문 211편 · 필터 해제
ICLEA: Interactive 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 GraphsAdversarial Attack against Cross-lingual Knowledge Graph Alignment
Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of cross-lingual entity alignment under ad…
Adversarial AttackEntity AlignmentTime-aware Graph Neural Network 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 AlignmentGraph Neural NetworkKnowledge GraphsPrincipled Representation Learning for Entity Alignment
Embedding-based entity alignment (EEA) has recently received great attention. Despite significant performance improvement, few efforts have been paid to facilitate understanding of EEA methods. Most existing studies rest…
Entity AlignmentEntity EmbeddingsRepresentation LearningHiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables. Hierarchical tables challenge numerical reasoning by complex hierarchical inde…
Entity AlignmentQuestion AnsweringText GenerationActiveEA: Active Learning for Neural Entity Alignment
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods -- neural EA models -- rely on training with seed alignmen…
Active LearningEntity AlignmentKnowledge GraphsTime-aware Relational Graph Attention Network for Temporal Knowledge Graph Embeddings
Embedding-based representation learning approaches for knowledge graphs (KGs) have been mostly designed for static data. However, many KGs involve temporal data, which creates the need for new representation learning app…
Entity AlignmentGraph AttentionKnowledge Graph EmbeddingsKnowledge Graphs+1From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment
Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs, which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance…
Entity AlignmentHiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables. Hierarchical tables challenge existing methods by hierarchical indexing, as we…
DescriptiveEntity AlignmentQuestion AnsweringText GenerationAre Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness
Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs. However, most existing EA methods have poor scalability and are unable to cope with large-…
Entity AlignmentGraph SamplingGlobal entity alignment with Gated Latent Space Neighborhood Aggregation
“Existing entity alignment models mainly use the topology structure of the original knowledge graph and have achieved promising performance. However they are still challenged by the heterogeneous topological neighborhood…
Entity AlignmentEntity EmbeddingsEchoEA: Echo Information between Entities and Relations for Entity Alignment
Entity alignment (EA) plays an important role in automatically integrating knowledge graphs (KGs) from multiple sources. Recent approaches based on Graph Neural Network (GNN) obtain entity representation from relation in…
AttributeEntity AlignmentGraph EmbeddingGraph Neural Network+2A Self-supervised Method for Entity Alignment
Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing large-scale KGs. Over the course of its development, supervision has been consid…
Contrastive LearningEntity AlignmentKnowledge GraphsSelf-Supervised LearningMulti-modal Entity Alignment in Hyperbolic Space
Many AI-related tasks involve the interactions of data in multiple modalities. It has been a new trend to merge multi-modal information into knowledge graph(KG), resulting in multi-modal knowledge graphs (MMKG). However,…
Entity AlignmentKnowledge GraphsMulti-modal Entity AlignmentKnowing the No-match: Entity Alignment with Dangling Cases
This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the proble…
Abstention PredictionEntity AlignmentKnowledge GraphsOntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities. Current methods have explored and utilized the graph structure, the entity names and attributes, but ignore the ontology (or ont…
Entity AlignmentGraph EmbeddingKnowledge Graph EmbeddingMultilingual Knowledge Graph Completion with Joint Relation and Entity Alignment
Knowledge Graph Completion (KGC) predicts missing facts in an incomplete Knowledge Graph. Almost all of existing KGC research is applicable to only one KG at a time, and in one language only. However, different language …
Entity AlignmentKnowledge Graph CompletionRelationCross-lingual Entity Alignment with Adversarial Kernel Embedding and Adversarial Knowledge Translation
Cross-lingual entity alignment, which aims to precisely connect the same entities in different monolingual knowledge bases (KBs) together, often suffers challenges from feature inconsistency to sequence context unawarene…
AttributeEntity AlignmentKnowledge GraphsTranslationBoosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining
Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is the pivotal step to KGs integration, also known as \emph{entity alignment} (EA). However, most existing EA methods are inefficient and poor in …
Entity AlignmentKnowledge GraphsA Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning
In the last few years, the interest in knowledge bases has grown exponentially in both the research community and the industry due to their essential role in AI applications. Entity alignment is an important task for enr…
AttributeEntity AlignmentRepresentation Learning