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Knowledge Base Completion

21 papers with code · Knowledge Base
Subtask of Knowledge Base

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.

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Greatest papers with code

Canonical Tensor Decomposition for Knowledge Base Completion

ICML 2018 Accenture/AmpliGraph

The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem.

KNOWLEDGE BASE COMPLETION LINK PREDICTION

Modeling Relational Data with Graph Convolutional Networks

17 Mar 2017tkipf/gae

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

GRAPH CLASSIFICATION INFORMATION RETRIEVAL KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION

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

2 Jun 2015glorotxa/SME

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

KNOWLEDGE BASE COMPLETION LINK PREDICTION

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

NAACL 2018 cai-lw/KBGAN

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 KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

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

14 Sep 2017nle-ml/mmkb

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

An overview of embedding models of entities and relationships for knowledge base completion

23 Mar 2017Sujit-O/pykg2vec

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

KNOWLEDGE BASE COMPLETION LINK PREDICTION

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

ACL 2019 deepakn97/relationPrediction

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 EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

Association of Computational Linguistics (ACl) 2019 2019 deepakn97/relationPrediction

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

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

NAACL 2018 daiquocnguyen/ConvKB

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

STransE: a novel embedding model of entities and relationships in knowledge bases

NAACL 2016 datquocnguyen/STransE

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

KNOWLEDGE BASE COMPLETION LINK PREDICTION