Multi-view entity alignment combining triples and text attributes

doi:10.11772/j.issn.1001-9081.2024050703

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (6): 1793-1800.DOI: 10.11772/j.issn.1001-9081.2024050703

• Artificial intelligence • Previous Articles

Multi-view entity alignment combining triples and text attributes

Sheping ZHAI¹^,², Yan HUANG¹(), Qing YANG¹, Rui YANG¹

^1.School of Computer Science and Technology，Xi’an University of Posts and Telecommunications，Xi’an Shaanxi 710121，China
^2.Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing （Xi’an University of Posts and Telecommunications），Xi’an Shaanxi 710121，China

Received:2024-05-30 Revised:2024-07-30 Accepted:2024-08-28 Online:2024-09-09 Published:2025-06-10
Contact: Yan HUANG
About author:ZHAI Sheping， born in 1971， Ph. D.， professor. His research interests include semantic computing， blockchain.
HUANG Yan， born in 2000， M. S. candidate. Her research interests include knowledge graph.
YANG Qing， born in 2000， M. S. candidate. Her research interests include knowledge graph.
YANG Rui， born in 1976， M. S.， lecturer. Her research interests include knowledge graph.
Supported by:
National Natural Science Foundation of China(61373116);Communication Soft Science Project of Ministry of Industry and Information Technology(2017-R-22);Shaanxi Province Key Research and Development Program(2022GY-038);Scientific Research Program of Shaanxi Education Department(18JK0697);Innovation and Entrepreneurship Training Program for College Students in Shaanxi Province(202211664053);Xi’an University of Posts and Telecommunications Graduate Student Innovation Fund(CXJJYL2022052)

融合三元组和文本属性的多视图实体对齐

翟社平¹^,², 黄妍¹(), 杨晴¹, 杨锐¹

^1.西安邮电大学计算机学院，西安 710721
^2.陕西省网络数据分析与智能处理重点实验室（西安邮电大学），西安 710121

通讯作者: 黄妍
作者简介:翟社平（1971—），男，陕西宝鸡人，教授，博士，CCF高级会员，主要研究方向：语义计算、区块链
黄妍（2000—），女，陕西渭南人，硕士研究生，主要研究方向：知识图谱 muhy0402@163.com
杨晴（2000—），女，陕西渭南人，硕士研究生，主要研究方向：知识图谱
杨锐（1976—），女，陕西咸阳人，讲师，硕士，主要研究方向：知识图谱。
基金资助:
国家自然科学基金资助项目(61373116);工业和信息化部通信软科学项目(2017-R-22);陕西省重点研发计划项目(2022GY-038);陕西省教育厅科学研究计划项目(18JK0697);陕西省大学生创新创业训练计划项目(202211664053);西安邮电大学研究生创新基金资助项目(CXJJYL2022052)

Abstract

Abstract:

Entity Alignment （EA） is to identify entities referring to the same thing in the Knowledge Graphs （KGs） of different sources. Most of the existing EA models focus on characteristics of the entities themselves， some of the models introduce entity relationship and attribute information to assist in alignment. However， these models ignore potential neighborhood information and semantic information in the entities. In order to solve the above problems， a Multi-view EA model combining triples and text attributes （MultiEA） was proposed. In the proposed model， entity information was divided into multiple views to achieve alignment. For the lack of neighborhood information， Graph Convolutional Network （GCN） and translation model were used to learn relationship information embedded in entities in parallel. Aiming at the lack of semantic information， word embedding and pre-trained language model were adopted to learn semantic information of attribute text. Experimental results show that on the three sub-datasets of DBP15K， compared to the baseline model EPEA （Entity-Pair Embedding Approach for KG alignment） that yields the optimal results， the Hits@1 value of the proposed model is increased by 2.18，1.36 and 0.96 percentage points， respectively， and the Mean Reciprocal Rank （MRR） of the proposed model is improved by 2.4，0.9 and 0.5 percentage points， respectively， indicating the effectiveness of the proposed model.

Key words: Entity Alignment (EA), knowledge embedding, attention mechanism, dependency syntactic parsing, BERT (Bidirectional Encoder Representations from Transformers)

摘要：

实体对齐（EA）旨在识别不同来源的知识图谱（KG）中指代相同的实体。现有的EA模型大多关注实体自身的特征，部分模型引入了实体的关系和属性信息辅助实现对齐，然而这些模型忽视了实体中潜在的邻域信息和语义信息。为了解决上述问题，提出一种融合三元组和文本属性的多视图EA模型（MultiEA）。所提模型将实体信息分为多个视图以实现对齐。针对缺少邻域信息的问题，采用图卷积网络（GCN）与翻译模型来并行学习嵌入实体的关系信息；针对缺少语义信息的问题，采用词嵌入与预训练语言模型学习属性文本的语义信息。实验结果表明，在DBP15K的3个子数据集上，相较于得到最优结果的基线模型EPEA（Entity-Pair Embedding Approach for KG alignment），所提模型的Hits@1值分别提升了2.18、1.36和0.96个百分点，平均倒数排名（MRR）分别提升了2.4、0.9和0.5个百分点，验证了所提模型的有效性。

关键词: 实体对齐, 知识嵌入, 注意力机制, 依存句法分析, BERT

CLC Number:

TP391.1

Sheping ZHAI, Yan HUANG, Qing YANG, Rui YANG. Multi-view entity alignment combining triples and text attributes[J]. Journal of Computer Applications, 2025, 45(6): 1793-1800.

翟社平, 黄妍, 杨晴, 杨锐. 融合三元组和文本属性的多视图实体对齐[J]. 《计算机应用》唯一官方网站, 2025, 45(6): 1793-1800.

Figures/Tables 9

Fig. 1 Overall framework of MultiEA

Fig. 2 DP-BERT model

Tab. 1 DBP15K dataset introduction

语言	实体数	关系数	属性数	关系三元组数	属性三元组数
ZH	66 496	2 830	8 113	153 929	379 684
EN	98 125	2 317	7 173	237 674	567 755
FR	66 858	1 379	4 547	192 191	528 665
EN	105 889	2 209	6 422	278 590	576 543
JA	65 744	2 043	5 882	164 373	354 619
EN	95 680	2 096	6 066	233 319	497 230

Tab. 2 Experimental results comparison of MultiEA model and baseline models on DBP15K dataset

模型	$D B P 15 K Z H ‑ E N$			$D B P 15 K J A ‑ E N$			$D B P 15 K F R ‑ E N$
模型	Hits@1	Hits@10	MRR	Hits@1	Hits@10	MRR	Hits@1	Hits@10	MRR
MTransE	30.83	61.41	0.364	27.86	57.45	0.349	24.41	55.55	0.335
IPTransE	40.60	73.50	0.516	36.70	69.30	0.474	33.30	68.50	0.451
JAPE	41.18	74.46	0.490	36.25	68.50	0.476	32.39	66.68	0.430
AWUN	75.10	88.30	0.796	80.50	92.40	0.848	91.50	97.40	0.938
RDGCN	70.75	84.55	0.746	76.74	89.54	0.812	88.64	95.72	0.911
NMN	73.30	86.90	—	78.50	91.20	—	90.20	96.70	—
MultiKE	50.90	57.60	0.523	39.30	48.90	0.426	63.90	71.20	0.665
RoadEA	82.40	89.60	0.846	86.80	90.50	0.886	81.00	88.40	0.831
GALA	78.10	86.24	0.811	82.80	90.73	0.855	92.70	96.97	0.942
RALG	83.54	94.75	0.876	87.23	96.58	0.906	94.83	98.91	0.964
EPEA	88.50	95.30	0.911	92.40	96.90	0.942	95.50	98.60	0.967
MultiEA	90.68	97.43	0.935	93.76	97.83	0.951	96.46	98.73	0.972

Tab. 2 Experimental results comparison of MultiEA model and baseline models on DBP15K dataset

模型	$D B P 15 K Z H ‑ E N$			$D B P 15 K J A ‑ E N$			$D B P 15 K F R ‑ E N$
模型	Hits@1	Hits@10	MRR	Hits@1	Hits@10	MRR	Hits@1	Hits@10	MRR
MTransE	30.83	61.41	0.364	27.86	57.45	0.349	24.41	55.55	0.335
IPTransE	40.60	73.50	0.516	36.70	69.30	0.474	33.30	68.50	0.451
JAPE	41.18	74.46	0.490	36.25	68.50	0.476	32.39	66.68	0.430
AWUN	75.10	88.30	0.796	80.50	92.40	0.848	91.50	97.40	0.938
RDGCN	70.75	84.55	0.746	76.74	89.54	0.812	88.64	95.72	0.911
NMN	73.30	86.90	—	78.50	91.20	—	90.20	96.70	—
MultiKE	50.90	57.60	0.523	39.30	48.90	0.426	63.90	71.20	0.665
RoadEA	82.40	89.60	0.846	86.80	90.50	0.886	81.00	88.40	0.831
GALA	78.10	86.24	0.811	82.80	90.73	0.855	92.70	96.97	0.942
RALG	83.54	94.75	0.876	87.23	96.58	0.906	94.83	98.91	0.964
EPEA	88.50	95.30	0.911	92.40	96.90	0.942	95.50	98.60	0.967
MultiEA	90.68	97.43	0.935	93.76	97.83	0.951	96.46	98.73	0.972

Tab. 3 Comparison of ablation experimental results on DBP15K sub-datasets

模型	$D B P 15 K Z H ‑ E N$			$D B P 15 K J A ‑ E N$			$D B P 15 K F R ‑ E N$
模型	Hits@1	Hits@10	MRR	Hits@1	Hits@10	MRR	Hits@1	Hits@10	MRR
MultiEA-Name	82.80	88.41	0.869	81.83	89.61	0.857	83.26	88.15	0.891
MultiEA-Rel	71.34	80.93	0.746	72.65	79.86	0.724	71.19	81.42	0.753
MultiEA-Attr	72.50	81.72	0.793	72.37	83.64	0.801	71.92	82.96	0.785
MultiEA-Name-Rel	85.62	90.61	0.904	86.97	91.72	0.916	86.03	91.49	0.923
MultiEA-Name-Attr	88.15	93.76	0.917	88.86	94.55	0.928	89.31	95.73	0.945
MultiEA	90.68	97.43	0.935	93.76	97.83	0.951	96.46	98.73	0.972

Tab. 3 Comparison of ablation experimental results on DBP15K sub-datasets

模型	$D B P 15 K Z H ‑ E N$			$D B P 15 K J A ‑ E N$			$D B P 15 K F R ‑ E N$
模型	Hits@1	Hits@10	MRR	Hits@1	Hits@10	MRR	Hits@1	Hits@10	MRR
MultiEA-Name	82.80	88.41	0.869	81.83	89.61	0.857	83.26	88.15	0.891
MultiEA-Rel	71.34	80.93	0.746	72.65	79.86	0.724	71.19	81.42	0.753
MultiEA-Attr	72.50	81.72	0.793	72.37	83.64	0.801	71.92	82.96	0.785
MultiEA-Name-Rel	85.62	90.61	0.904	86.97	91.72	0.916	86.03	91.49	0.923
MultiEA-Name-Attr	88.15	93.76	0.917	88.86	94.55	0.928	89.31	95.73	0.945
MultiEA	90.68	97.43	0.935	93.76	97.83	0.951	96.46	98.73	0.972

Fig. 3 Influence of different seed proportion on MultiEA model and baseline models

Fig.4 Comparison of different relationship embedding balance parameters

Fig. 5 Comparison of different embedding dimensions

Tab. 4 Hits@1 results comparison of different entities in DBP15K sub-datasets

对齐实体	实体名称辅助对齐	实体关系辅助对齐	实体属性辅助对齐	MultiEA 辅助对齐
http：//zh.dbpedia.org/resource/汉字 http：//dbpedia.org/resource/Chinese	82.4	85.1	86.7	93.2
http：//zh.dbpedia.org/resource/美利坚合众国 http：//dbpedia.org/resource/USA	70.3	84.2	84.9	94.6
http：//dbpedia.org/resource/China http：//fr.dbpedia.org/resource/Chine	93.6	94.2	94.7	95.1
http：//dbpedia.org/resource/Vampire Detective http：//fr.dbpedia.org/resource/Détective vampire	89.2	91.1	91.6	94.8
http：//ja.dbpedia.org/resource/風の谷のナウシカ http：//dbpedia.org/resource/Nausicaä of the Valley of the Wind （film）	71.6	92.3	93.1	96.5
http：//ja.dbpedia.org/resource/眠れる森の美女（1959 film） http：//dbpedia.org/resource/Sleeping Beauty（1959年の映画）	90.3	94.1	95.7	97.3

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Multi-view entity alignment combining triples and text attributes

融合三元组和文本属性的多视图实体对齐

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