Browse > Knowledge Base > Knowledge Graphs > Knowledge Graph Completion

# Knowledge Graph Completion Edit

13 papers with code · Knowledge Base

No evaluation results yet. Help compare methods by submit evaluation metrics.

# Knowledge Graph Completion via Complex Tensor Factorization

22 Feb 2017ttrouill/complex

In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.

156

# One-Shot Relational Learning for Knowledge Graphs

Knowledge graphs (KGs) are the key components of various natural language processing applications.

107

# TuckER: Tensor Factorization for Knowledge Graph Completion

28 Jan 2019ibalazevic/TuckER

Knowledge graphs are structured representations of real world facts.

97

# Learning Sequence Encoders for Temporal Knowledge Graph Completion

In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations.

81

# Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

17 Feb 2019TaoMiner/joint-kg-recommender

In this paper, we jointly learn the model of recommendation and knowledge graph completion.

74

# A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization

In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object).

63

# Open-World Knowledge Graph Completion

Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking.

62

# DSKG: A Deep Sequential Model for Knowledge Graph Completion

30 Oct 2018nju-websoft/DSKG

Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of $(subject, relation, object)$.

47

# ProjE: Embedding Projection for Knowledge Graph Completion

16 Nov 2016Sujit-O/pykg2vec

In this work, we present a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph's entities and edges, and through subtle, but important, changes to the standard loss function.

33

# Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence

9 May 2017thunlp/CKRL

Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.

24