• Publications
  • Influence
Translating Embeddings for Modeling Multi-relational Data
TLDR
TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Expand
Learning Sequence Encoders for Temporal Knowledge Graph Completion
TLDR
This work utilizes recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods to incorporate temporal information. Expand
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
TLDR
The 30M Factoid Question-Answer Corpus is presented, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. Expand
Learning Graph Representations with Embedding Propagation
TLDR
Embedding Propagation is an unsupervised learning framework for graph-structured data with significantly fewer parameters and hyperparameters that is competitive with and often outperforms state of the art unsuper supervised and semi-supervisedLearning methods on a range of benchmark data sets. Expand
KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
TLDR
KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features, and it is shown that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. Expand
Composing Relationships with Translations
TLDR
An extension of TransE is proposed that learns to explicitly model composition of relationships via the addition of their corresponding translation vectors and it is shown empirically that this allows to improve performance for predicting single relationships as well as compositions of pairs of them. Expand
Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases
TLDR
This paper proposes TATEC, a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and then combined, and shows that this approach outperforms existing methods on different types of relationships by achieving state-of-the-art results on four benchmarks of the literature. Expand
Effective Blending of Two and Three-way Interactions for Modeling Multi-relational Data
TLDR
This paper proposes a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and jointly fine-tuned, and achieves state-of-the-art results on two benchmarks of the literature. Expand
MMKG: Multi-Modal Knowledge Graphs
TLDR
This work presents MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs that has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs. Expand
TransRev: Modeling Reviews as Translations from Users to Items
TLDR
TransRev is an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective and outperforms state of the artRecommender systems on a large number of benchmark data sets. Expand
...
1
2
3
...