mlsanityData to 7 Oct 2026

Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review

arXiv 2203.09280 ↗First posted 17 Mar 2022cs.DB cs.AIcited by 5

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Abstract (arXiv)

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 from different KGs is to find which subgraphs refer to the same real-world entity. Recently, embedding methods have been used for entity alignment tasks, that learn a vector-space representation of entities which preserves their similarity in the original KGs. A wide variety of supervised, unsupervised, and semi-supervised methods have been proposed that exploit both factual (attribute based) and structural information (relation based) of entities in the KGs. Still, a quantitative assessment of their strengths and weaknesses in real-world KGs according to different performance metrics and KG characteristics is missing from the literature. In this work, we conduct the first meta-level analysis of popular embedding methods for entity alignment, based on a statistically sound methodology. Our analysis reveals statistically significant correlations of different embedding methods with various meta-features extracted by KGs and rank them in a statistically significant way according to their effectiveness across all real-world KGs of our testbed. Finally, we study interesting trade-offs in terms of methods' effectiveness and efficiency.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 24 of its 63 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

24 of 63 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  5. 2016Complex Embeddings for Simple Link Predictioncited by 897
  6. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  7. 2017Poincar'e Embeddings for Learning Hierarchical Representationscited by 674
  8. 2016Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignmentcited by 128
  9. 2020A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphscited by 119
  10. 2019Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphscited by 106
  11. 2019Multi-Channel Graph Neural Network for Entity Alignmentcited by 86
  12. 2017End-to-End Neural Ad-hoc Ranking with Kernel Poolingcited by 85
12 more
  1. 2019Multi-view Knowledge Graph Embedding for Entity Alignmentcited by 74
  2. 2020Relational Reflection Entity Alignmentcited by 53
  3. 2018Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignmentcited by 49
  4. 2021A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learningcited by 31
  5. 2018Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmarkcited by 21
  6. 2021A Survey of Knowledge Graph Embedding and Their Applicationscited by 17
  7. 2020An Industry Evaluation of Embedding-based Entity Alignmentcited by 15
  8. 2020On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methodscited by 13
  9. 2021Medical Entity Disambiguation Using Graph Neural Networkscited by 10
  10. 2020QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnosticscited by 7
  11. 2017An Exploration of Word Embedding Initialization in Deep-Learning Taskscited by 6
  12. 2021EAGER: Embedding-Assisted Entity Resolution for Knowledge Graphscited by 5

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Cited by year

Citing papers per year: 2023: 1; 2024: 4.

5 papers.

  1. 2024Uncertainty Management in the Construction of Knowledge Graphs: a Surveycited by 5
  2. 2023Universal Preprocessing Operators for Embedding Knowledge Graphs with Literalscited by 4
  3. 2024NALA: an Effective and Interpretable Entity Alignment Methodcited by 1
  4. 2024Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learningcited by 1
  5. 2024Detecting text level intellectual influence with knowledge graph embeddingscited by 0