KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

Abstract

We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN.

UAI 2018

References in corpus (1)

• Learning Multi-Relational Semantics Using Neural-Embedding Models

Cited by in corpus (10)

• A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
• CoKE: Contextualized Knowledge Graph Embedding
• GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
• Learning Sequence Encoders for Temporal Knowledge Graph Completion
• Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes
• Embedding Cardinality Constraints in Neural Link Predictors
• Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications
• HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations
• Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation
• Knowledge Graph Completion to Predict Polypharmacy Side Effects