Unsupervised Entity Alignment Using Attribute Triples and Relation Triples

He, Fuzhen; Li, Zhixu; Yang, Qiang; Liu, An; Liu, Guanfeng; Zhao, Lei; Zhang, Min; Chen, Zhigang

doi:10.1007/978-3-030-18576-3_22

Cited by 39 publications

(19 citation statements)

References 21 publications

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“…As far as we know, the current embedding-based entity alignment methods mostly rely on the seed mappings, whose roles are introduces in Section 2.1, for supervised or semi-supervised learning. Specially, we can consider some heuristic rules with, for example, string and attribute matching to generate the seed mappings, as done by the method IMUSE (He et al, 2019), but the impact of the seed mappings is similar and the study of such impact also benefit the distant supervision methods.…”

Section: Seed Mappingsmentioning

confidence: 99%

An Industry Evaluation of Embedding-based Entity Alignment

Zhang¹,

Liu²,

Chen³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

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Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage. In this study, we evaluate those state-of-the-art methods in an industrial context, where the impact of seed mappings with different sizes and different biases is explored. Besides the popular benchmarks from DBpedia and Wikidata, we contribute and evaluate a new industrial benchmark that is extracted from two heterogeneous knowledge graphs (KGs) under deployment for medical applications. The experimental results enable the analysis of the advantages and disadvantages of these alignment methods and the further discussion of suitable strategies for their industrial deployment.

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Section: Seed Mappingsmentioning

confidence: 99%

An Industry Evaluation of Embedding-based Entity Alignment

Zhang¹,

Liu²,

Chen³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

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show abstract

“…We adopt twelve competitive KG alignment methods as the baselines. They can be categorized into (i) embedding-based models that include MTransE , IPTransE [Zhu et al, 2017], GCNAlign [Wang et al, 2018], BootEA , RSN4EA , IMUSE [He et al, 2019], MultiKE [Zhang et al, 2019], and RDGCN [Wu et al, 2019], (ii) conventional systems that include PARIS and LogMap, and (iii) two simple matching models using either the edit distance (denoted by STR-Match) or the word embedding similarity (denoted by EMB-Match) between entity names. We adopt the implementations of the embedding-based models from OpenEA [Sun et al, 2020] and also the same dataset division: 20%, 10%, and 70% of the entity mappings for training, validation, and testing, respectively.…”

Section: Experimental Settingmentioning

confidence: 99%

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

Zhang

Chen³

et al. 2021

Preprint

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Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embedding-based models and the conventional reasoning and lexical matching based systems. The former compute the similarity of entities via their cross-KG embeddings, but they usually rely on an ideal supervised learning setting for good performance and lack appropriate reasoning to avoid logically wrong mappings; while the latter address the reasoning issue but are poor at utilizing the KG graph structures and the entity contexts. In this study, we aim at combining the above two solutions and thus propose an iterative framework named PRASE that is based on probabilistic reasoning and semantic embedding. It learns the KG embeddings via entity mappings from a probabilistic reasoning system named PARIS, and feeds the resultant entity mappings and embeddings back into PARIS for augmentation. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.

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“…The merit lies in the capabilities of resolving the aforementioned heterogeneity and simplifying knowledge reasoning [36,88]. Motivated by such success, a new research field, called embedding-based entity alignment, has emerged [10] and attracted massive attention recently [8,9,25,29,66,80,81,84,89,92,102].…”

Section: Introductionmentioning

confidence: 99%

A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs

Sun,

Zhang,

et al. 2020

Preprint

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Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings. In this paper, we conduct a comprehensive experimental study of this emerging field. This study surveys 23 recent embeddingbased entity alignment approaches and categorizes them based on their techniques and characteristics. We further observe that current approaches use different datasets in evaluation, and the degree distributions of entities in these datasets are inconsistent with real KGs. Hence, we propose a new KG sampling algorithm, with which we generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation. This study also produces an open-source library, which includes 12 representative embedding-based entity alignment approaches. We extensively evaluate these approaches on the generated datasets, to understand their strengths and limitations. Additionally, for several directions that have not been explored in current approaches, we perform exploratory experiments and report our preliminary findings for future studies. The benchmark datasets, open-source library and experimental results are all accessible online and will be duly maintained.

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Unsupervised Entity Alignment Using Attribute Triples and Relation Triples

Cited by 39 publications

References 21 publications

An Industry Evaluation of Embedding-based Entity Alignment

An Industry Evaluation of Embedding-based Entity Alignment

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs

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