Entity Alignment For Knowledge Graphs: Progress, Challenges, and Empirical Studies

Abstract: Entity Alignment (EA) identifies entities across databases that refer to the same entity. Knowledge graph-based embedding methods have recently dominated EA techniques. Such methods map entities to a low-dimension space and align them based on their similarities. With the corpus of EA methodologies growing rapidly, this paper presents a comprehensive analysis of various existing EA methods, elaborating their applications and limitations. Further, we distinguish the methods based on their underlying algorithms and the information they incorporate to learn entity representations. Based on challenges in industrial datasets, we bring forward $4$ research questions (RQs). These RQs empirically analyse the algorithms from the perspective of \textit{Hubness, Degree distribution, Non-isomorphic neighbourhood,} and \textit{Name bias}. For Hubness, where one entity turns up as the nearest neighbour of many other entities, we define an $h$-score to quantify its effect on the performance of various algorithms. Additionally, we try to level the playing field for algorithms that rely primarily on name-bias existing in the benchmarking open-source datasets by creating a low name bias dataset. We further create an open-source repository for $14$ embedding-based EA methods and present the analysis for invoking further research motivations in the field of EA.

• The paper reviews embedding, translation, and GCN-based methods that align entities across diverse knowledge graphs.
• It demonstrates iterative and semi-supervised strategies that balance model complexity with computational efficiency.
• The study underscores challenges like scalability, heterogeneity, and scarce labeled data, supported by empirical benchmark evaluations.

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This paper provides a comprehensive survey of the field of Entity Alignment (EA) for Knowledge Graphs (KGs). EA aims to identify entities across different KGs that refer to the same real-world object. This is crucial for integrating information from disparate knowledge sources, which is a common task in building large-scale knowledge bases or enhancing recommendation systems.

Key Concepts and Progress:

• Problem Definition: The core task is to find pairs of equivalent entities $(e_1, e_2)$ where $e_1 \in KG_1$ and $e_2 \in KG_2$.
• Approaches Surveyed: The paper categorizes EA methods into several groups:
  • Embedding-based Methods: These are currently dominant. They learn low-dimensional vector representations (embeddings) for entities and relations in each KG, often within a shared vector space. Alignment is then performed by finding entity pairs with similar embeddings, typically using distance metrics like L1 or L2. Techniques involve jointly learning embeddings or mapping embeddings from different KGs into a common space using transformations (e.g., linear transformations, non-linear mappings). Popular models like MTransE, JAPE, GCN-Align, BootEA, and MultiKE fall into this category.
  • Translation-based Models: Inspired by TransE, these models interpret relations as translations in the embedding space ($h + r \approx t$). MTransE extends this by learning transition matrices to map embeddings between KGs.
  • Graph Neural Network (GCN)-based Models: These methods leverage GNNs to capture the graph structure more effectively when generating entity embeddings. Models like GCN-Align use GCNs to encode neighborhood information, improving embedding quality and alignment accuracy.
  • Semi-supervised and Iterative Methods: Techniques like BootEA use bootstrapping, starting with a small seed set of aligned entities and iteratively predicting new alignments and retraining the model. This helps overcome the scarcity of labeled alignment data.
  • Handling Heterogeneity: Some methods address challenges arising from different schema, languages, or levels of completeness between KGs.
• Evaluation Metrics: Common metrics include Hits@k (the proportion of correctly aligned entities ranked within the top k candidates), Mean Rank (MR), and Mean Reciprocal Rank (MRR).

Implementation Considerations:

• Data Preprocessing: KGs often require cleaning and normalization. Entity and relation mapping requires creating dictionaries. Seed alignment pairs are crucial for supervised and semi-supervised methods.
• Embedding Training: This involves selecting an embedding model (e.g., TransE, RotatE variations) and a loss function (e.g., margin-based loss, negative sampling loss). Hyperparameters like embedding dimension, learning rate, and margin need careful tuning.
• Alignment Strategy: After training, entity similarity is computed (e.g., cosine similarity, Euclidean distance). Candidate pairs are ranked, and techniques like CSLS (Cross-domain Similarity Local Scaling) can be used to refine results and mitigate the hubness problem (where some entities are nearest neighbors to many others).
• Computational Requirements: Training embedding models, especially GNN-based ones on large KGs, can be computationally intensive, requiring significant memory and potentially GPU acceleration. Iterative methods add to the training time.

Challenges Highlighted:

• Scalability: Applying EA methods to very large KGs remains a challenge due to computational costs.
• Heterogeneity: KGs differ significantly in structure, density, schema, and language, making alignment difficult.
• Lack of Labeled Data: Obtaining sufficient seed alignments for training supervised models is often hard. Semi-supervised and unsupervised methods are crucial but often less accurate.
• Complex Alignments: Handling 1-to-N, N-to-1, and N-to-N alignments (where one entity in a KG might correspond to multiple entities in another, or vice versa) is often overlooked by standard methods.
• Dynamic KGs: Real-world KGs evolve; EA methods need to adapt to updates without full retraining.

Empirical Studies and Insights:

• The paper often summarizes results from various benchmark datasets (e.g., DBP15K, DWY100K).
• Embedding-based methods, particularly those using GNNs and iterative strategies, generally achieve state-of-the-art performance.
• The choice of negative sampling strategy and distance metric significantly impacts performance.
• There's a trade-off between model complexity and performance; more sophisticated models capture structure better but require more data and computation.
• The paper emphasizes the need for more realistic evaluation settings and datasets that better reflect real-world challenges like heterogeneity and scale.

In essence, the paper (2205.08777) serves as a guide for practitioners looking to implement EA. It outlines the dominant techniques (especially embedding-based approaches), discusses practical implementation details like training and evaluation, highlights key obstacles like scalability and data scarcity, and points towards future research directions needed to make EA more robust and applicable in diverse real-world scenarios.