Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment

2022-01-25 · Xinhang Li, Yong Zhang, Chunxiao Xing

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 entity alignment via measuring the similarity between entity embeddings. However, they failed to make good use of the relation semantic information due to the trade-off problem caused by the different objectives of learning knowledge embedding and neighborhood consensus. To address this problem, we propose Relational Knowledge Distillation for Entity Alignment (RKDEA), a Graph Convolutional Network (GCN) based model equipped with knowledge distillation for entity alignment. We adopt GCN-based models to learn the representation of entities by considering the graph structure and incorporating the relation semantic information into GCN via knowledge distillation. Then, we introduce a novel adaptive mechanism to transfer relational knowledge so as to jointly learn entity embedding and neighborhood consensus. Experimental results on several benchmarking datasets demonstrate the effectiveness of our proposed model.

📄 PDF Abstract BibTeX arXiv:2201.11249

Code (0)

등록된 구현이 없습니다.

Tasks

Benchmarking Entity AlignmentEntity EmbeddingsKnowledge Distillation Knowledge GraphsRelation

Methods 이 논문이 사용한 방법론

GCN A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of [convolutional neural…

Knowledge Distillation A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions.…

HittER: Hierarchical Transformers for Knowledge Graph Embeddings

2020-08-28 · EMNLP 2021 11 · Sanxing Chen, Xiaodong Liu, Jianfeng Gao, Jian Jiao 외

This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity…

Knowledge Graph Embeddings Link Prediction Question Answering Relation

PatchMatch-Based Neighborhood Consensus for Semantic Correspondence

2021-06-19 · CVPR 2021 1 · Jae Yong Lee, Joseph DeGol, Victor Fragoso, Sudipta N. Sinha

We address estimating dense correspondences between two images depicting different but semantically related scenes. End-to-end trainable deep neural networks incorporating neighborhood consensus cues are currently th…

Semantic correspondence

Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction

2026-05-18 · Luu Huu Phuc, Ratan Bahadur Thapa, Mojtaba Nayyeri, Jingcheng Wu 외 arxiv

We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Attention Network (RGAT) to improve link prediction in knowledge graphs. Wh…

Knowledge Graphs Link Prediction

Relational Message Passing for Knowledge Graph Completion

2020-02-17 · Hongwei Wang, Hongyu Ren, Jure Leskovec

Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. In this work, we propose a relational message passing method for knowledge graph completion. Different from existing emb…

Knowledge Graph Completion Relation Relation Prediction

Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering

2026-04-26 · Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante arxiv

Learning low-dimensional representations from multi-view relational data is challenging when underlying geometries differ across views. We propose Bary-GWMDS, a Gromov-Wasserstein-based method that operates directly on d…