Collective Entity Alignment via Adaptive Features

Zeng, Weixin; Zhao, Xiang; Tang, Jiuyang; Lin, Xuemin

doi:10.1109/icde48307.2020.00191

Cited by 85 publications

(68 citation statements)

References 30 publications

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“…Concretely, we use: (1) Lev, which aligns entities using Levenshtein distance [37], a string metric for measuring the difference between two sequences; and (2) Embed, which aligns entities according to the cosine similarity between the name embeddings (averaged word embedding) of two entities. Following [65], we use the pre-trained fastText embeddings [2] as word embeddings, and for multilingual KG pairs, we use the MUSE word embeddings [10].…”

Section: Datasetsmentioning

confidence: 99%

An Experimental Study of State-of-the-Art Entity Alignment Approaches

Zhao

Zeng

Tang

et al. 2020

IEEE Trans. Knowl. Data Eng.

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Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e.g., question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different settings (i.e., datasets, information used as input, etc.). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories. Next, we judiciously evaluate these solutions on a wide range of use cases, based on their effectiveness, efficiency and robustness. Finally, we construct a new EA dataset to mirror the real-life challenges of alignment, which were largely overlooked by existing literature. This study strives to provide a clear picture of the strengths and weaknesses of current EA approaches, so as to inspire quality follow-up research. ! 1. As where we are standing, EA can be deemed as a special case of entity resolution (ER), which recalls a pile of literature (to be discussed in Section 2.2). Thus, some ER methods (with minor adaptation to handle EA) are also involved in this study to ensure the comprehensiveness of the research.this article, we provide an empirical evaluation of state-of-the-art EA approaches with the following features: (1) Fair comparison within and across categories. Almost all recent studies [5], [24], [38], [55], [60], [61], [62], [63],[67] are confined to comparing with only a subset of methods. In addition, different approaches follow different settings: some merely use the KG structure for alignment, while others also utilize additional information; some align KGs in one pass, while others employ an iterative (re-)training strategy. Although a direct comparison of these methods, as reported in the literature, demonstrates the overall effectiveness of the solutions, a more preferable and fairer practice would be to group these methods into categories and then compare the results both within and across categories.In this study, we include most state-of-the-art methods for lateral comparison, including those very recent efforts that have not yet been compared with others before. By dividing them into three groups and conducting detailed analysis on both intraand inter-group evaluations, we are able to better position these approaches and assess their effectiveness.(2) Comprehensive evaluation on representative datasets. To evaluate the performance of EA systems, several datasets have been constructed, which can be broadly categorized into cross-lingual benchmarks, represented by DBP15K [53], and mono-lingual benchmarks, represented by DWY100K [54]. A very recent study [24] points out th...

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Section: Datasetsmentioning

confidence: 99%

An Experimental Study of State-of-the-Art Entity Alignment Approaches

Zhao

Zeng

Tang

et al. 2020

IEEE Trans. Knowl. Data Eng.

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“…State-of-the-art EA solutions [9][10][11][12] assume that equivalent entities usually possess similar neighboring information.…”

Section: Examplementioning

confidence: 99%

Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling

et al. 2022

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Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase knowledge coverage and quality. Recent years have witnessed a rapid increase of EA frameworks. However, state-of-the-art solutions tend to rely on labeled data for model training. Additionally, they work under the closed-domain setting and cannot deal with entities that are unmatchable. To address these deficiencies, we offer an unsupervised framework that performs entity alignment in the open world. Specifically, we first mine useful features from the side information of KGs. Then, we devise an unmatchable entity prediction module to filter out unmatchable entities and produce preliminary alignment results. These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment. Finally, the progressive learning framework gradually improves the quality of structural embeddings and enhances the alignment performance. Furthermore, noticing that the pseudo-labeled data are of various qualities, we introduce the concept of confidence to measure the probability of an entity pair of being true and develop a confidence-based unsupervised EA framework . Our solutions do not require labeled data and can effectively filter out unmatchable entities. Comprehensive experimental evaluations validate the superiority of our proposals .

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“…( 2) Word-level: These methods average the pre-trained entity name vectors to construct the initial features: GM-Align , RDGCN (Wu et al, 2019a), HGCN (Wu et al, 2019b), DAT (Zeng et al, 2020b), DGMC (Fey et al, 2020). (3) Char-level: These EA methods further adopt the char-level textual features: At-trGNN , CEA (Zeng et al, 2020a), EPEA . For our proposed method, SEU(word) and SEU(char) represent the model only using the word and char features as the inputs, respectively.…”

Section: Baselinesmentioning

confidence: 99%

From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment

Mao¹,

Wang²,

Wu³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs (Knowledge Graphs), which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance improvements on several public datasets. However, existing GNN-based EA methods inevitably inherit poor interpretability and low efficiency from neural networks. Motivated by the isomorphic assumption of GNN-based methods, we successfully transform the cross-lingual EA problem into an assignment problem. Based on this re-definition, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. Extensive experiments have been conducted to show that our proposed unsupervised approach even beats advanced supervised methods across all public datasets while having high efficiency, interpretability, and stability.

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Collective Entity Alignment via Adaptive Features

Cited by 85 publications

References 30 publications

An Experimental Study of State-of-the-Art Entity Alignment Approaches

An Experimental Study of State-of-the-Art Entity Alignment Approaches

Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling

From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment

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