Knowledge Base Completion

42 papers with code • 0 benchmarks • 0 datasets

Knowledge base completion is the task which automatically infers missing facts by reasoning about the information already present in the knowledge base. A knowledge base is a collection of relational facts, often represented in the form of "subject", "relation", "object"-triples.


Greatest papers with code

Fast Linear Model for Knowledge Graph Embeddings

facebookresearch/fastText 30 Oct 2017

This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings.

General Classification Knowledge Base Completion +2

Modeling Relational Data with Graph Convolutional Networks

tkipf/gae 17 Mar 2017

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

General Classification Graph Classification +5

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

deepakn97/relationPrediction ACL 2019

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).

Knowledge Base Completion Knowledge Graph Completion +2

Modeling Relation Paths for Representation Learning of Knowledge Bases

Mrlyk423/Relation_Extraction EMNLP 2015

Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space.

Knowledge Base Completion Relation Extraction +2

A survey of embedding models of entities and relationships for knowledge graph completion

Sujit-O/pykg2vec 23 Mar 2017

Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.

Knowledge Base Completion Knowledge Graph Completion +1

Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases

glorotxa/SME 2 Jun 2015

This paper tackles the problem of endogenous link prediction for Knowledge Base completion.

Knowledge Base Completion Link Prediction

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

nle-ml/mmkb 14 Sep 2017

We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features.

Knowledge Base Completion Representation Learning

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

cai-lw/KBGAN NAACL 2018

This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.

Knowledge Base Completion Knowledge Graph Embedding +3

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

daiquocnguyen/ConvKB NAACL 2018

This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps.

Knowledge Base Completion Link Prediction