Embedding models for episodic knowledge graphs

Ma, Yunpu; Tresp, Volker; Daxberger, Erik

doi:10.1016/j.websem.2018.12.008

Cited by 83 publications

(47 citation statements)

References 12 publications

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“…We show that our model significantly outperforms its Euclidean counterpart and other state-of-the-art ap-proaches on three benchmark datasets of temporal KGs, which demonstrates the significance of geometrical spaces for the temporal knowledge graph completion task. 2018b), andHyTE (Dasgupta et al, 2018). We use the ADAM optimizer (Kingma and Ba, 2014) and the cross-entropy loss to train the temporal KG models.…”

Section: Discussionmentioning

confidence: 99%

“…Temporal KG Embedding Recently, there have been some attempts of incorporating time information in temporal KGs to improve the performance of link prediction. Ma et al (2018b) developed extensions of static knowledge graph models by adding a timestamp embedding to the score functions. Also, Leblay and Chekol (2018) proposed TTransE by incorporating time representations into the score function of TransE in different ways.…”

Section: Knowledge Graph Embeddingmentioning

confidence: 99%

“…From the static KG embedding models, we use TransE (Bordes et al, 2013), DistMult (Yang et al, 2014), and Com-plEx (Trouillon et al, 2016) where we compress temporal knowledge graphs into a static, cumulative graph by ignoring the time information. From the temporal KG embedding models, we compare the performance of our model with several stateof-the-art methods, including TTransE (Leblay and Chekol, 2018), TDistMult/TComplEx (Ma et al, 2018b), andHyTE (Dasgupta et al, 2018).…”

Section: Experimental Set-upmentioning

confidence: 99%

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DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

Han¹,

Chen²,

Ma³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Temporal KGs often exhibit multiple simultaneous non-Euclidean structures, such as hierarchical and cyclic structures. However, existing embedding approaches for temporal KGs typically learn entity representations and their dynamic evolution in the Euclidean space, which might not capture such intrinsic structures very well. To this end, we propose Dy-ERNIE, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data. Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs. Besides, to capture the evolutionary dynamics of temporal KGs, we let the entity representations evolve according to a velocity vector defined in the tangent space at each timestamp. We analyze in detail the contribution of geometric spaces to representation learning of temporal KGs and evaluate our model on temporal knowledge graph completion tasks. Extensive experiments on three real-world datasets demonstrate significantly improved performance, indicating that the dynamics of multi-relational graph data can be more properly modeled by the evolution of embeddings on Riemannian manifolds.

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Section: Discussionmentioning

confidence: 99%

Section: Knowledge Graph Embeddingmentioning

confidence: 99%

Section: Experimental Set-upmentioning

confidence: 99%

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DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

Han¹,

Chen²,

Ma³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…Some TKGE models are extended from TransE, e.g., TTransE (Leblay and Chekol, 2018), TA-TransE (García-Durán et al, 2018), HyTE (Dasgupta et al, 2018 and ATiSE . Another part of TKGE models are temporal extensions of DistMult, e.g., Know-Evolve (Trivedi et al, 2017), TDistMult (Ma et al, 2018) and TA-DistMult (García-Durán et al, 2018). Similar to TransE and DistMult, these TKGE models have issues with capturing reflexive relations or asymmetric relations.…”

Section: Related Workmentioning

confidence: 99%

“…To tackle this problem, TKG embedding (TKGE) models encode time information in their embeddings. Such TKGE models (Trivedi et al, 2017;Leblay and Chekol, 2018;García-Durán et al, 2018;Ma et al, 2018;Dasgupta et al, 2018;Jin et al, 2019) were shown to have better performances on link prediction over TKGs than traditional KGE models. However, most of the existing TKGE models are the extensions of TransE (Bordes et al, 2013) and DistMult (Yang et al, 2014), and thus are not fully expressive for some relation patterns (Sun et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

Xu¹,

Nayyeri²,

Alkhoury³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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In the last few years, there has been a surge of interest in learning representations of entities and relations in knowledge graph (KG). However, the recent availability of temporal knowledge graphs (TKGs) that contain time information for each fact created the need for reasoning over time in such TKGs. In this regard, we present a new approach of TKG embedding, TeRo, which defines the temporal evolution of entity embedding as a rotation from the initial time to the current time in the complex vector space. Specially, for facts involving time intervals, each relation is represented as a pair of dual complex embeddings to handle the beginning and the end of the relation, respectively. We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferring various relation patterns over time. Experimental results on four different TKGs show that TeRo significantly outperforms existing state-of-the-art models for link prediction. In addition, we analyze the effect of time granularity on link prediction over TKGs, which as far as we know has not been investigated in previous literature.

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Introduction to the MDATA Model

Jia

et al. 2021

MDATA: A New Knowledge Representation Model

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Embedding models for episodic knowledge graphs

Cited by 83 publications

References 12 publications

DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

Introduction to the MDATA Model

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