Relational Reflection Entity Alignment

Mao, Xin; Wang, Wenting; Xu, Hao; Wu, Yuanbin; Lan, Man

doi:10.1145/3340531.3412001

Cited by 104 publications

(98 citation statements)

References 25 publications

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“…In this work, we compare our proposed models with three strong translational baseline models and three state-of-the-art GNN-based models including MTransE (Chen et al, 2017), JAPE , AlignE (Sun et al, 2018), GCN-Align (Wang et al, 2018), MRAEA (Mao et al, 2020a) and RREA (Mao et al, 2020b). We choose AlignE instead of BootEA since we do not use iterative learning for other models including our proposed models.…”

Section: Methodsmentioning

confidence: 99%

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

Xu¹,

Su²,

Lehmann³

2021

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

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Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that contain time information created the need for reasoning over time in such TKGs. Existing embeddingbased entity alignment approaches disregard time information that commonly exists in many large-scale KGs, leaving much room for improvement. In this paper, we focus on the task of aligning entity pairs between TKGs and propose a novel Time-aware Entity Alignment approach based on Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of different KGs into a vector space and use GNNs to learn entity representations. To incorporate both relation and time information into the GNN structure of our model, we use a time-aware attention mechanism which assigns different weights to different nodes with orthogonal transformation matrices computed from embeddings of the relevant relations and timestamps in a neighborhood. Experimental results on multiple real-world TKG datasets show that our method significantly outperforms the state-ofthe-art methods due to the inclusion of time information. Our datasets and source code are available at https://github.com/ soledad921/TEA-GNN

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Section: Methodsmentioning

confidence: 99%

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

Xu¹,

Su²,

Lehmann³

2021

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

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“…-BootEA [23], which employs the bootstrapping strategy to iteratively label likely entity alignment as train-ing data for learning alignment-oriented KG embeddings; -TransEdge [15], which proposes a novel edge-centric embedding model that contextualizes relation representations in terms of specific head-tail entity pairs; -MRAEA [42], which models entity embeddings by attending over the node's incoming and outgoing neighbors and its connected relations' meta semantics; -SSP [44], which jointly leverages the global KG structure and entity-specific relational triples for better entity alignment. -RREA [45], which leverages relational reflection transformation to obtain relation specific embeddings for each entity and achieves effective entity alignment.…”

Section: Competitorsmentioning

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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“…Sun et al (Sun et al, 2017) proposed the DBP15K dataset that provided cross-lingual entity alignments between Japanese-English, French-English and Chinese-English versions of DBPedia respectively. This work has led to the proposal of various entity alignment systems (Sun et al, 2017(Sun et al, , 2020Mao et al, 2020;Liu et al, 2021a;Nguyen et al, 2020a;Liu et al, 2021b;Lu et al, 2021;Qi et al, 2021) that, given a pair of Knowledge Graphs (KGs), seek to discover an injective mapping between the entities of the corresponding KGs. The common approach used by these systems involves ranking the most similar entities in KG-2 for each entity in KG-1.…”

Section: Related Workmentioning

confidence: 99%

VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment

Iyer¹,

Agarwal²,

Kumar³

2021

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

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Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc. State-of-the-art (SOTA) Ontology Alignment systems typically use naive domain-dependent approaches with handcrafted rules or domainspecific architectures, making them unscalable and inefficient. In this work, we propose VeeAlign, a Deep Learning based model that uses a novel dual-attention mechanism to compute the contextualized representation of a concept which, in turn, is used to discover alignments. By doing this, not only is our approach able to exploit both syntactic and semantic information encoded in ontologies, it is also, by design, flexible and scalable to different domains with minimal effort. We evaluate our model on four different datasets from different domains and languages, and establish its superiority through these results as well as detailed ablation studies. The code and datasets used are available at https://github.com/Remorax/VeeAlign.

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Relational Reflection Entity Alignment

Cited by 104 publications

References 25 publications

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

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

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

VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment

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