Cross-knowledge-graph entity alignment via relation prediction

Huang, Hongren; Li, Chen; Peng, Xutan; He, Lifang; Guo, Shu; Peng, Hao; Wang, Lihong; Li, Jianxin

doi:10.1016/j.knosys.2021.107813

Cited by 19 publications

(7 citation statements)

References 32 publications

(63 reference statements)

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“…It shows that the non-isomorphism does exist commonly in two KGs and addressing it will benefit the CLEA. 4) Our method has an obvious improvement than other nonisomorphism baselines, including AliNet [17], KE-GCN [23], DAEA [15], KE-GCN [23] and RpAlign [16]. H@1 of our method is improved by 14.5%, 13.2%, and 14.9% averagely on three datasets.…”

Section: Resultsmentioning

confidence: 81%

“…TransE-based baselines include MTransE [11], IPTransE [26], and NAEA [27]. GNN-based baselines include GCN-Align [22], MuGCN [2], GAT [20], R-GCN [13], MuGCN [2], MRAEA [8], RREA [9], Dual-AMN [7] , PSR [6], Sparse [3] and RpAlign [16]. There are also some baselines focusing on the non-isomorphic CLEA, including of AliNet [17], KE-GCN [23] and DAEA [15].…”

Section: A Datasets and Baselinesmentioning

confidence: 99%

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Cross-Knowledge Graph Relation Completion for Non-isomorphic Cross‐lingual Entity Alignment

Zhang,

Lu,

et al. 2023

International Conferences on Distributed Multimedia Systems

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The Cross-Lingual Entity Alignment (CLEA) aims to find the aligned entities that refer to the same identity from two Knowledge Graphs (KGs) in different languages. In real-world applications, the neighborhood structures of the same entities in different KGs tend to be non-isomorphic, which makes the entity representation contain diverse semantics information and poses a great challenge for CLEA. In this paper, we address this challenge from two perspectives. On the one hand, cross-KG relation completion rules are designed with the alignment constraint of entities and relations to improve the isomorphism of two KGs. On the other hand, a representation method combining isomorphic weights is designed to include more isomorphic semantics for counterpart entities, which will benefit CLEA. Experimental results show that our model can improve the isomorphism of two KGs and the alignment performance, especially for two non-isomorphic KGs.

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Section: Resultsmentioning

confidence: 81%

Section: A Datasets and Baselinesmentioning

confidence: 99%

Cross-Knowledge Graph Relation Completion for Non-isomorphic Cross‐lingual Entity Alignment

Zhang,

Lu,

et al. 2023

International Conferences on Distributed Multimedia Systems

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“…Compared with traditional entity alignment methods, methods based on GCNs not only require relatively less human involvement in the process of feature construction, but also such methods can be extended to large knowledge graphs. Methods based on GCNs [6,[20][21][22][23][24][25][26][27][28][29][30][31] usually combine embedding methods to embed the data to be processed into a unified vector space, and the method based on embedding has been well applied and developed [32,33]. JAPE [6] sets two embedding modules, namely Structure Embedding (SE) and Attribute Embedding (AE), which jointly embed the structures of two knowledge graphs into a unified vector space, and then use the attribute correlation in the knowledge graph to further improvement.…”

Section: Entity Alignment Based On Graph Convolutional Networkmentioning

confidence: 99%

“…In this process, when obtaining the number of neighbor entities of each entity, the existing method [24][25][26][27] directly ignores the situation that the head and tail entities have the same name in the relation triple, but this situation may exist in the real corpus. For example, with relational triples , it is obvious that the head and tail entities in this triple have the same name, but it cannot be judged that these two entities refer to the same thing in the real world.…”

Section: Neighbor Entity Screeningmentioning

confidence: 99%

Cross-Language Entity Alignment for Joint Entity Screening and Dual Relation Graph

Zhang

Wang

2022

Preprint

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In knowledge graphs of different languages, entity alignment is usuallyinterfered by problems such as structural heterogeneity and languagedifference. The emergence of these problems seriously affects the taskof entity alignment. Existing methods continuously improve alignmentstrategies, but often ignore the impact of triples with the same headand tail entity names on the entity alignment task. This paper takes thehead entity in the triple as the central entity, and proposes an entityalignment method that effectively utilizes neighbor entities with thesame entity name as the central entity. We take advantage of the multi-order properties of Graph Convolutional Neural Networks to processentities, relationships and attributes in the source and target knowl-edge graphs through knowledge structure embedding and entity nameembedding. We define the primal graph and the dual relation graphrespectively, the former is composed of two knowledge graphs together,and the latter is constructed by the association of relations in triplesto strengthen the role of relations. We use the primal graph and thedual relation graph to process the entities and relationships in knowledgegraphs. We compare the output vectors obtained by the two meth-ods, and then select the optimal output vector, combine the entityID to remove the neighbor entities that are completely equivalent tothe central entity, and finally obtain the final alignment result throughneighbor sampling and the similarity calculation of the correspondingentity. Experimental results on real datasets show that our methodsignificantly helps improve the task of cross-lingual entity alignment.

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“…It is essential in various real-world applications, including identifying equivalent entities between knowledge graphs (KGs) 11 – 14 . To improve the abstraction of node features for matching, training GNNs in supervised or semi-supervised models has become the standard approach 15 – 17 . However, there are three main challenges when performing approximate subgraph matching in the knowledge graph.…”

Section: Introductionmentioning

confidence: 99%

Modification and completion of geological structure knowledge graph based on pattern matching

Lu,

Xu,

Zhang

2024

Sci Rep

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As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out.

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Cross-knowledge-graph entity alignment via relation prediction

Cited by 19 publications

References 32 publications

Cross-Knowledge Graph Relation Completion for Non-isomorphic Cross‐lingual Entity Alignment

Cross-Knowledge Graph Relation Completion for Non-isomorphic Cross‐lingual Entity Alignment

Cross-Language Entity Alignment for Joint Entity Screening and Dual Relation Graph

Modification and completion of geological structure knowledge graph based on pattern matching

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