Papers Entity Alignment
“Entity Alignment” 태그가 달린 논문 211편 · 필터 해제
Neighborhood Matching Network for Entity Alignment
Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural …
Entity AlignmentGraph SamplingKnowledge GraphsCross-lingual Entity Alignment with Incidental Supervision
Much research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different languagespecific KGs that refer to the same real-w…
Entity AlignmentKnowledge GraphsTransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs
Learning knowledge graph (KG) embeddings has received increasing attention in recent years. Most embedding models in literature interpret relations as linear or bilinear mapping functions to operate on entity embeddings.…
Entity AlignmentEntity EmbeddingsKnowledge GraphsLink Prediction+1A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encode…
BenchmarkingEntity AlignmentKnowledge GraphsOn the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment. In the current experimental setting, multiple differ…
Entity AlignmentInformativenessKnowledge GraphsLink PredictionMRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge Graph
Entity alignment to find equivalent entities in cross-lingual Knowledge Graphs (KGs) plays a vital role in automatically integrating multiple KGs. Existing translation-based entity alignment methods jointly model the cro…
Entity AlignmentEntity EmbeddingsGraph Neural NetworkKnowledge Graphs+1Deep Graph Matching Consensus
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial rank…
Entity AlignmentGraph MatchingGraph Neural NetworkKnowledge Graphs+1Active Learning for Entity Alignment
In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets. Different strategies to select informative instances for the human labeler build the core of our framework. We…
Active LearningEntity AlignmentCoordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity. T…
Entity AlignmentCollective Entity Alignment via Adaptive Features
Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, c…
Entity AlignmentKnowledge GraphsKnowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart …
Entity AlignmentKnowledge GraphsKnowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task. Variants of GCN are used in…
Entity AlignmentKnowledge GraphsModeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment
Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP a…
Entity AlignmentKnowledge GraphsSemi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model
Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages, which may benefit many knowledge-driven applications. It is challenging due to the heterogeneity of KGs and l…
Entity AlignmentGraph AttentionKnowledge GraphsmodelAligning Cross-Lingual Entities with Multi-Aspect Information
Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a source language with their counterparts in ta…
Entity AlignmentEntity EmbeddingsKnowledge GraphsJointly Learning Entity and Relation Representations for Entity Alignment
Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs). Recent developments in the field often take an embedding-based approach to model the structural informati…
Entity AlignmentEntity EmbeddingsKnowledge GraphsRelationMulti-Channel Graph Neural Network for Entity Alignment
Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented k…
Entity AlignmentGraph Neural NetworkRelation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG …
Entity AlignmentEntity EmbeddingsKnowledge GraphsRelationMulti-view Knowledge Graph Embedding for Entity Alignment
We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as att…
Entity AlignmentGraph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLearning to Exploit Long-term Relational Dependencies in Knowledge Graphs
We study the problem of knowledge graph (KG) embedding. A widely-established assumption to this problem is that similar entities are likely to have similar relational roles. However, existing related methods derive KG em…
Entity AlignmentKnowledge Graphs