Entity Alignment
106 papers with code • 10 benchmarks • 8 datasets
Entity Alignment is the task of finding entities in two knowledge bases that refer to the same real-world object. It plays a vital role in automatically integrating multiple knowledge bases.
Note: results that have incorporated machine translated entity names (introduced in the RDGCN paper) or pre-alignment name embeddings are considered to have used extra training labels (both are marked with "Extra Training Data" in the leaderboard) and are not adhere to a comparable setting with others that have followed the original setting of the benchmark.
Source: Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
The task of entity alignment is related to the task of entity resolution which focuses on matching structured entity descriptions in different contexts.
Latest papers
The Power of Noise: Toward a Unified Multi-modal Knowledge Graph Representation Framework
In this work, to evaluate models' ability to accurately embed entities within MMKGs, we focus on two widely researched tasks: Multi-modal Knowledge Graph Completion (MKGC) and Multi-modal Entity Alignment (MMEA).
ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects.
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.
Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
In this paper, we present a propagation perspective to analyze weakly supervised EA and explain the existing aggregation-based EA models.
Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment
This study introduces a novel approach, DESAlign, which addresses these issues by applying a theoretical framework based on Dirichlet energy to ensure semantic consistency.
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
This generalized gradient flow enables TFP to harness the multi-view structural information of KGs.
Learning High-Quality and General-Purpose Phrase Representations
The framework employs phrase type classification as an auxiliary task and incorporates character-level information more effectively into the phrase representation.
Generating Explanations to Understand and Repair Embedding-based Entity Alignment
In this paper, we present the first framework that can generate explanations for understanding and repairing embedding-based EA results.
MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
More importantly, in pseudo mapping learning, we propose a bi-directional voting (BDV) strategy that fuses the alignment decisions in different directions to estimate the uncertainty via the joint matching confidence score.
Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment
To address these challenges, we propose a novel MMEA transformer, called MoAlign, that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task.