Factorizing YAGO: scalable machine learning for linked data

By Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel. 2012

443 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Learning Entity and Relation Embeddings for Knowledge Graph Completion (2015, methodology): “In experiments we will also compare with RESCAL, a collective matrix factorization model presented in (Nickel, Tresp, and Kriegel 2011; 2012).”
A semantic matching energy function for learning with multi-relational data (2013, methodology): “RESCAL has been applied to the knowledge base YAGO (Nickel et al. 2012) and hence showed to scale well on data with large numbers of entities (few millions).”
End-to-end Differentiable Proving (2017, methodology): “Current state-of-the-art methods for automated Knowledge Base (KB) completion use neural link prediction models to learn distributed vector representations of symbols ( i.e. subsymbolic representations) for scoring fact triples [1–7].”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, methodology): “Recent embedding models TransE (Bordes et al., 2013b) and NTN (Socher et al., 2013) have shown state-of-the-art prediction performance compared to tensor factorization methods such as RESCAL (Nickel et al., 2012).”
Commonsense Knowledge Base Completion (2016, methodology): “Our methods are similar to past work on KBC (Mintz et al., 2009; Nickel et al., 2011; Lao et al., 2011; Nickel et al., 2012; Riedel et al., 2013; Gardner et al., 2014; West et al., 2014), particularly methods based on distributed representations and neural networks (Socher et al., 2013; Bordes et…”
A Review of Relational Machine Learning for Knowledge Graphs (2015, methodology): “This property can be exploited for entity resolution and has also enabled large-scale hierarchical clustering on relational data [59, 60].”
Traversing Knowledge Graphs in Vector Space (2015, background): “An elegant solution to incompleteness is using vector space representations: Controlling the dimensionality of the vector space forces generalization to new facts (Nickel et al., 2011; Nickel et al., 2012; Socher et al., 2013; Riedel et al., 2013; Nee-lakantan et al., 2015).”
Holographic Embeddings of Knowledge Graphs (2015, background): “For a review of machine learning on knowledge graphs see also Nickel et al. (2015). Existing models for knowledge graphs are based on the following compositional operators:”
Iterative Entity Alignment via Joint Knowledge Embeddings (2017, methodology): “There are existing some other methods to learn knowledge representations: RESCAL [Nickel et al., 2011; 2012] and HOLE [Nickel et al., 2016] are based on tensor factorization with relations considered to be matrices.”
A Survey of Fake News (2020, background): “…feature models , which assume that the existence of KB triples is conditionally independent given latent features and parameters (e.g., RESCAL [Nickel et al. 2012], NTN [Socher et al. 2013], DistMult [Yang et al. 2014], TransE [Bordes et al. 2013], TransH [Wang et al. 2014], TransR [Lin et…”
Knowledge graph refinement: A survey of approaches and evaluation methods (2016, methodology): “[72] propose the use of matrix factorization to predict entity types in YAGO.”
Probabilistic Reasoning via Deep Learning: Neural Association Models (2016, methodology): “In order to take advantage of these sparse knowledge bases, the state-of-the-art approaches for knowledge graph embedding usually adopt simple linear models, such as RESCAL [ Nickel et al. , 2012 ] for statistical relational learning (SRL).”
OpenKE: An Open Toolkit for Knowledge Embedding (2018, background): “…Jenatton et al., 2012; Yang et al., 2015; Liu et al., 2017), neural models (Socher et al., 2013; Dong et al., 2014), matrix factorization models (Nickel et al., 2011, 2012, 2016; Trouillon et al., 2016), and translation models (Bordes et al., 2013; Wang et al., 2014; Lin et al., 2015; Ji et…”
Translation-based Recommendation (2017, methodology): “…et al. , 2015; Wang et al. , 2014]) have achieved state-of-the-art accuracy and scalability, in contrast to those achieved by traditional embedding methods relying on tensor decomposition or collective matrix factorization (e.g. [Nickel et al. , 2011; Nickel et al. , 2012; Singh and Gordon, 2008]).”
SimplE Embedding for Link Prediction in Knowledge Graphs (2018, background): “In RESCAL [28], the embedding vector for each entity e is v e ∈ R d and for each relation r is v r ∈ R d × d and the similarity function for a triple ( h , r , t ) is v r · vec ( v h ⊗ v t ) , where ⊗ represents the outer product of two vectors and vec ( .”
Lifted Rule Injection for Relation Embeddings (2016, methodology): “Current successful methods for automated knowledge base construction tasks heavily rely on learned distributed vector representations (Nickel et al., 2012; Riedel et al., 2013; Socher et al., 2013; Chang et al., 2014; Neelakantan et al., 2015; Toutanova et al., 2015; Nickel et al., 2015; Verga et al”