Knowledge 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 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.
Code (1)
Tasks
AttributeEntity AlignmentGraph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsQuestion AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Embedding-Based Entity Alignment Using Relation Structural Similarity
Entity alignment aims to find entities in different knowledge graphs that semantically represent the same real-world entity. Recently, embedding-based entity alignment methods, which represent knowledge graphs as low-dim…
Entity AlignmentEntity EmbeddingsGraph EmbeddingKnowledge Graph Embedding+2Jointly 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+2TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this t…
Entity AlignmentEntity EmbeddingsKnowledge GraphsIs Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods
Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum. In order to make use of multiple KG em…
Entity DisambiguationKnowledge Graph CompletionKnowledge GraphsQuestion Answering+1AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this t…
Entity AlignmentEntity EmbeddingsKnowledge Graphs