Papers with Entity alignment
Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)
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| Challenge: | Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs). |
| Approach: | They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets. |
Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment (D19-1)
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| Challenge: | Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. |
| Approach: | They propose to use dot product-based functions to define dot products over embeddings to better capture semantics of 1-N, N-1 and N-N relations. |
| Outcome: | The proposed framework outperforms existing methods on multilingual datasets. |
Multi-Channel Graph Neural Network for Entity Alignment (P19-1)
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| Challenge: | Existing methods to learn alignment-oriented knowledge graph embeddings suffer from structural heterogeneity and limited seed alignments. |
| Approach: | They propose a multi-channel Graph Neural Network model to learn alignment-oriented knowledge graph embeddings by encoding two KGs via multiple channels. |
| Outcome: | The proposed model is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. |
A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs (2022.coling-1)
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| Challenge: | Existing methods for EA between temporal KGs incorporate relational and temporal information into entity embeddings. |
| Approach: | They propose a method to generate unsupervised alignment seeds using temporal information from TKGs. |
| Outcome: | The proposed method outperforms the previous methods by using temporal information. |
A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment (2023.findings-acl)
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| Challenge: | Entity alignment is the task of identifying corresponding entities across different knowledge graphs (KGs). |
| Approach: | They propose an unsupervised entity alignment framework that leverages the Fused Gromov-Wasserstein distance to compare KG semantics and KG structural information. |
| Outcome: | The proposed framework surpasses 21 competitive baselines, including cutting-edge methods, without supervision or hyper-parameter tuning. |
Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning (2025.coling-main)
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| Challenge: | Entity alignment (EA) aims to match identical entities across knowledge graphs (KGs) Graph neural network-based entity alignment methods have achieved promising results in Euclidean space, but KGs often contain complex local and hierarchical structures, which are hard to represent in a single space. |
| Approach: | They propose a method which unifies dual-space embedding to preserve the intrinsic structure of KGs. |
| Outcome: | The proposed method achieves state-of-the-art in structure-based EA on benchmark datasets. |
Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding (2025.findings-naacl)
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| Challenge: | Existing methods for integrating knowledge graphs rely on entity and relation embeddings . Fig. 1 shows how to decode knowledge graph in under 6 seconds . |
| Approach: | They propose a framework that only utilizes entity embeddings to decode knowledge graphs. |
| Outcome: | The proposed framework reconstructs KG representation by maximizing smoothness of entity embeddings. |
Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model (D19-1)
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| Challenge: | Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages. |
| Approach: | They propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model to make better use of seed alignments to propagate over the entire graphs with KG-based constraints. |
| Outcome: | The proposed method can make better use of seed alignments to propagate over entire graphs with KG-based constraints. |
Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate (2026.findings-acl)
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| Challenge: | Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs). |
| Approach: | They propose a reliable EA framework based on multi-agent debate that improves embedding quality and introduces a two-stage multi-role debate mechanism to enhance reliability. |
| Outcome: | The proposed framework improves embedding quality and the reasoning capability of LLMs while enabling more efficient debate-based reasoning. |
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism (2022.acl-long)
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| Challenge: | Existing methods focus on graph representation learning, but decoding is a key part of the process. |
| Approach: | They propose an EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI) they combine two sets of isomorphic equations to enhance the decoding process . |
| Outcome: | The proposed algorithm can deliver significant performance improvements even on the most advanced methods while the extra required time is less than 3 seconds. |
Adaptive Graph Convolutional Network for Knowledge Graph Entity Alignment (2022.findings-emnlp)
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| Challenge: | Entity alignment (EA) aims to identify equivalent entities from different Knowledge Graphs (KGs) noisy neighbors of entities transfer invalid information, drown out equivalent information, and ultimately reduce the performance of EA. |
| Approach: | They propose a method to deal with neighbor noises to reduce the performance of EA by capturing the differences and complementarities of multiple KGs. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods in supervised and unsupervised settings. |
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment (2020.emnlp-main)
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| Challenge: | Entity alignment (EA) aims at building a Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. |
| Approach: | They propose to use an attributed value encoder to partition a Knowledge Graph into subgraphs to model the various types of attribute triples efficiently. |
| Outcome: | The proposed method achieves significant improvements over 12 baselines in cross-lingual and monolingual datasets. |
Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs (2021.emnlp-main)
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| Challenge: | Existing embedding-based approaches disregard time information that exists in many large-scale knowledge graphs, leaving much room for improvement. |
| Approach: | They propose a Time-aware Entity Alignment approach that incorporates relation and time information into a vector space and uses Graph Neural Networks to learn entity representations. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on real-world TKG datasets due to the inclusion of time information. |
NALA: an Effective and Interpretable Entity Alignment Method (2024.findings-emnlp)
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| Challenge: | Existing embedding-based EA methods encode entities as embeddables and learn to align embeddibles. |
| Approach: | They propose to capture three types of logical inference paths with Non-Axiomatic Logic to iteratively align entities and relations by integrating the conclusions of the inference path. |
| Outcome: | The proposed method outperforms state-of-the-art methods in terms of Hits@1 on all three datasets of DBP15K with both supervised and unsupervised settings. |
EasyEA: Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs (2025.findings-acl)
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| Challenge: | Entity alignment (EA) aims to identify entities in different knowledge graphs (KGs) that represent the same real-world object. |
| Approach: | They propose an end-to-end EA framework based on large language models that requires no training to implement. |
| Outcome: | The proposed framework significantly reduces the reliance on seed entity pairs while achieving state-of-the-art (SOTA) performance on diverse datasets. |
Entity Profile Generation and Reasoning with LLMs for Entity Alignment (2025.findings-emnlp)
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| Challenge: | Entity alignment is a process of identifying and linking equivalent entities across knowledge graphs . only a small fraction of these entities are aligned . |
| Approach: | They propose a method that combines large language models with entity embeddings to align entities. |
| Outcome: | ProLEA is a method that combines large language models with entity embeddings to improve alignment accuracy, robustness, and explainability. |
How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)
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| Challenge: | Entity alignment (EA) is critical for knowledge graph (KG) integration. |
| Approach: | They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment. |
| Outcome: | The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment. |
EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment (2026.acl-long)
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| Challenge: | Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object. |
| Approach: | They propose to use large language models to integrate semantic knowledge into EA to identify entities across different knowledge graphs that refer to the same object. |
| Outcome: | The proposed agent outperforms existing methods and achieves state-of-the-art performance on three benchmark datasets. |