Translating Embeddings for Modeling Multi-relational Data

By Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Aaron Edward Weston, Oksana Yakhnenko. 2013

9,012 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning, Graph Learning

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Citations in the corpus

Word-Entity Duet Representations for Document Ranking (2017, methodology): “We choose the popular TransE model which is effective and efficient to be applied on large knowledge graphs [2].”
Language Models as Knowledge Bases? (2019, methodology): “To account for multiple valid objects for a subject-relation pair (i.e., for N-M relations), we follow Bordes et al. (2013) and remove from the candidates when ranking at test time all other valid objects in the training data other than the one we test.”
Knowledge Enhanced Contextual Word Representations (2019, methodology): “These methods broadly fall into two categories: translational distance models (Bordes et al., 2013; Wang et al., 2014b; Lin et al., 2015; Xiao et al., 2016) which use a distance-based scoring function, and linear models (Nickel et al., 2011; Yang et al., 2014; Trouillon et al., 2016; Dettmers et…”
Revisiting Semi-Supervised Learning with Graph Embeddings (2016, background): “TransE (Bordes et al., 2013) learns the embeddings of entities in a knowledge graph jointly with their relations.”
On Sampling Strategies for Neural Network-based Collaborative Filtering (2017, neutral): “This assumption is shattered by models that are defined under graph structure, with applications in social and knowledge graph mining [2], image caption ranking [20], and so on.”
FiLM: Visual Reasoning with a General Conditioning Layer (2017, methodology): “However, approaches from word embeddings, representation learning, and zero-shot learning can be applied to directly optimize (γ,β) for analogy-making (Bordes et al. 2013; Guu, Miller, and Liang 2015; Oh et al. 2017).”
Neural Approaches to Conversational AI (2018, neutral): “ reasoning in Sec. 3.4. 3.3 Embedding-based Methods To address the paraphrasing problem, embedding-based methods map entities and relations in a KB to continuous vectors in a neural space; see, e.g., Bordes et al. (2013); Socher et al. (2013); Yang et al. (2015); Yih et al. (2015b). This space can b”
ERNIE: Enhanced Language Representation with Informative Entities (2019, methodology): “To be speciﬁc, we represent { e 1 , . . . , e m } with their entity embeddings { e 1 , . . . , e m } , which are pre-trained by the effective knowledge embedding model TransE (Bor-des et al., 2013).”
Knowledge Graph Convolutional Networks for Recommender Systems (2019, background): “, TransE [1] and TransR [12] assume head + relation = tail ), which are more suitable for in-graph applications such as KG completion and link prediction rather than recommendation.”
Knowledge Graph Embedding by Translating on Hyperplanes (2014, methodology): “Please refer to the Introduction part of (Bordes et al. 2013b).”
Neural Collaborative Filtering (2017, background): “Along a similar line, learning the relations of two entities has been intensively studied in literature of knowledge graphs [2, 33].”
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, methodology): “l distance models, such as TransE [3], TransH [37], TransD [10], and TransR [14], exploit distance-based scoring functions when learning representations of entities and relations. For example, TransE [3] wants h+r ≈twhen (h,r,t)holds, where h, rand tare the corresponding representation vector of h, ”
DKN: Deep Knowledge-Aware Network for News Recommendation (2018, methodology): “In addition to translation-based methods [4, 18, 26, 48] used in DKN, researchers have also proposed many other models such as Structured Embedding [5], Latent Factor Model [17], Neural Tensor Network [37] and GraphGAN [42].”
Learning Entity and Relation Embeddings for Knowledge Graph Completion (2015, methodology): “As mentioned in Section " Introduction " , TransE (Bordes et al. 2013) wants h+r ≈ t when (h, r, t) holds.”
Integrating Learning and Reasoning with Deep Logic Models (2019, background): “In particular, similarly to [4], the constants are encoded into a”
Compositional Fairness Constraints for Graph Embeddings (2019, background): “, [5, 20, 22]) are also amenable to our framework.”
Relational inductive biases, deep learning, and graph networks (2018, background): “…Sanchez-Gonzalez et al., 2018) and multi-agent systems (Sukhbaatar et al., 2016; Hoshen, 2017; Kipf et al., 2018), to reason about knowledge graphs (Bordes et al., 2013; O˜noro-Rubio et al., 2017; Hamaguchi et al., 2017), to predict the chemical properties of molecules (Duvenaud et al., 2015;…”
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, methodology): “initial approach above as well as existing tools that include TRANS-E (Bordes et al. 2013), TRANS-H (Wang et al.”