Learning to Represent Knowledge Graphs with Gaussian Embedding

By Shizhu He, Kang Liu, Guoliang Ji, Jun Zhao. International Conference on Information and Knowledge Management, 2015

431 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/02e2059c328bd9fad4e676266435199663bed804

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Citations in the corpus

Knowledge Graph Completion with Adaptive Sparse Transfer Matrix (2016, methodology): “KG2E (He et al., 2015) uses Gaussian embedding to model the data uncertainty.”
Deep learning for noise-tolerant RDFS reasoning (2019, background): “Gaussian embeddings in this class such as KG2E [57] aim to model uncertainty in the entities and relations.”
Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking (2017, methodology): “Since our latent representations are distributions similarly to [4, 9] we employ the KL divergence as an asymmetric similarity measure – this gives the additional benefit of handling directed graphs in a sound way.”
From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction (2015, methodology): “KG2E [ He et al. , 2015 ] is a probabilistic embedding method for modeling the uncertainty in knowledge base.”
Analogical Inference for Multi-relational Embeddings (2017, background): “Unstructured (Bordes et al., 2013) 38.2 6.3 RESCAL (Nickel et al., 2011) 52.8 44.1 NTN (Socher et al., 2013) 66.1 41.4 SME (Bordes et al., 2012) 74.1 41.3 SE (Bordes et al., 2011) 80.5 39.8 LFM (Jenatton et al., 2012) 81.6 33.1 TransH (Wang et al., 2014) 86.7 64.4 TransE (Bordes et al., 2013) 89.2 4”
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, methodology): “Recently, ITransF [36] enables hidden concepts discovery and statistical strength transferring by learning associations between relations and concepts via sparse attention vectors, with scoring function deﬁned as where D ∈ R n d d is stacked concept projection matrices of entities and relations and ”
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking (2017, methodology): “Closest to our work, He et al. (2015) represent knowledge graphs and Dos Santos et al. (2016) study heterogeneous graphs for node classiﬁcation.”
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions (2016, methodology): “Also, KG2E (He et al. 2015) involves probabilistic analysis to characterize the uncertainty concepts of knowledge graph.”
Graph Neural Networks in Recommender Systems: A Survey (2020, neutral): “, word embedding [140], document embedding [41, 111], and network/graph embedding [9, 53, 159].”
Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, background): “KG2E [45] represents entities and relations as random vectors drawn from multivariate Gaussian distributions, i.”
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, methodology): “KG2E [28] leverages Gaussian distribution covariance, and TransG [29] uses Bayesian techniques for one-to-many relational facts.”
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions (2019, methodology): “For modeling uncertainty of learned representations, Gaussian distributions (He et al. 2015; Xiao, Huang, and Zhu 2016) have also been utilized.”
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings (2020, background): “…words, sentences, or knowledge base concepts with geometric objects such as regions (Erk, 2009; Vilnis et al., 2018; Li et al., 2019), densities (Vilnis & McCallum, 2014; He et al., 2015; Athiwaratkun & Wilson, 2018), and orderings (Vendrov et al., 2016; Lai & Hockenmaier, 2017; Li et al., 2017).”
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space (2018, methodology): “We also notice that the probabilistic framework KG2E KL(bern) (He et al., 2015) is quite powerful, which consistently outperforms its corresponding knowledge graph embedding model, showing the importance of modeling the uncertainties in knowledge graphs.”
Incorporating Knowledge Graph Embeddings into Topic Modeling (2017, positive): “Since cosine distance is typically used to measure similarity between entity embeddings (Ji et al. 2015; He et al. 2015; Yang et al. 2015) and some knowledge graph embeddings lie on a unit sphere ( 2 norm equals to 1) (Bordes et al. 2011; 2013; 2014; Yang et al. 2015; Garcia-Duran et al. 2016), we…”
Entity Embeddings of Categorical Variables (2016, methodology): “1 of [28]) to measure the likelihood of such a triplet, and the score function is used as the objective function for learning the embeddings.”