Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs

Xu, Chengjin; Su, Fenglong; Lehmann, Jens

doi:10.18653/v1/2021.emnlp-main.709

Cited by 21 publications

(25 citation statements)

References 30 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the formats of storing temporal information in TKGs are almost identical, the alignment information can be obtained easily, which naturally enhances the EA performance. Nevertheless, recent studies [53,54] that applies TKGs to EA still suffer from the following two problems.…”

Section: G S G Tmentioning

confidence: 99%

“…They [53,54] follow studies that applies KGs to EA, which cannot perform EA until the prealigned seeds are obtained. However, unlike KGs, temporal information in relational triples of TKGs is naturally aligned since they represent real-world time points or time periods [53,54]. This character of TKGs provides the opportunity of developing time-aware EA methods in an unsupervised fashion by treating temporal information as seed alignment.…”

Section: G S G Tmentioning

confidence: 99%

“…• Insufficient temporal information discovery. They [53,54] simply create embeddings for each timestamp, and treat the time intervals as relation types to enhance the graph learning process. These processes highly rely on the seed alignment, where the temporal information has not been fully exploited, and thus the accuracy of EA is limited.…”

Section: G S G Tmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Entity Alignment for Temporal Knowledge Graphs

Liu¹,

Wu²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA. However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch that effectively fuses the relational and temporal information for EA. DualMatch transfers EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which achieves EA without necessitating the seed alignment. DualMatch has two steps: (i) encoding temporal and relational information into embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Extensive experiments on three real-world TKG datasets offer the insight that DualMatch significantly outperforms the state-of-the-art methods.

show abstract

Section: G S G Tmentioning

confidence: 99%

Section: G S G Tmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Entity Alignment for Temporal Knowledge Graphs

Liu¹,

Wu²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Zhu et al (2021) proposed a new framework based on relation-aware graph attention networks to capture the interactions between entities and relations. Xu et al (2021) embeds entities, relations, and timestamps of different knowledge graphs into a vector space for entity alignment.…”

Section: Entity Alignmentmentioning

confidence: 99%

A Novel Compressed Sensing-Based Graph Isomorphic Network for Key Node Recognition and Entity Alignment

Zhao

Huang

Fan

et al. 2022

International Journal on Semantic Web and Information Systems

View full text Add to dashboard Cite

In recent years, the related research of entity alignment has mainly focused on entity alignment via knowledge embeddings and graph neural networks; however, these proposed models usually suffer from structural heterogeneity and the large-scale problem of knowledge graph. A novel entity alignment model based on graph isomorphic network and compressed sensing is proposed. First, for the problem of structural heterogeneity, graph isomorphic network encoder is applied in knowledge graph to capture structural similarity of entity relation. Second, for the problem of large scale, key node and community are integrated for priority entity alignment to improve execution speed. However, the exiting node importance ranking algorithm cannot accurately identify key node in knowledge graph. So the compressed sensing is adopted in node importance ranking to improve the accuracy of identifying key node. The authors have carried out several experiments to test the effect and efficiency of the proposed entity alignment model.

show abstract

“…To address these issues, several approaches have been put forward. Particularly, the time-aware entity alignment approach based on graph neural networks (TEA-GNN) (Xu et al, 2021) first designs a time-aware GNN to cope with TEA, which exploits a time-aware mechanism to introduce the time information into entity embeddings. The time-aware entity alignment using temporal relational attention (TREA) (Xu et al, 2022), on the other hand, incorporates temporal embeddings to enrich the entity embeddings and achieves state-of-the-art performance.…”

Section: Introductionmentioning

confidence: 99%

Active Temporal Knowledge Graph Alignment

Zhou

Zeng

et al. 2023

International Journal on Semantic Web and Information Systems

View full text Add to dashboard Cite

Entity alignment aims to identify equivalent entity pairs from different knowledge graphs (KGs). Recently, aligning temporal knowledge graphs (TKGs) that contain time information has aroused increasingly more interest, as the time dimension is widely used in real-life applications. The matching between TKGs requires seed entity pairs, which are lacking in practice. Hence, it is of great significance to study TKG alignment under scarce supervision. In this work, the authors formally formulate the problem of TKG alignment with limited labeled data and propose to solve it under the active learning framework. As the core of active learning is to devise query strategies to select the most informative instances to label, the authors propose to make full use of time information and put forward novel time-aware strategies to meet the requirement of weakly supervised temporal entity alignment. Extensive experimental results on multiple real-world datasets show that it is important to study TKG alignment with scarce supervision, and the proposed time-aware strategy is effective.

show abstract

Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs

Cited by 21 publications

References 30 publications

Unsupervised Entity Alignment for Temporal Knowledge Graphs

Unsupervised Entity Alignment for Temporal Knowledge Graphs

A Novel Compressed Sensing-Based Graph Isomorphic Network for Key Node Recognition and Entity Alignment

Active Temporal Knowledge Graph Alignment

Contact Info

Product

Resources

About