Knowledge Graph Completion
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Knowledge Graph Completion via Complex Tensor Factorization
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.
KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING
One-Shot Relational Learning for Knowledge Graphs
EMNLP 2018
• xwhan/One-shot-Relational-Learning
• 
Knowledge graphs (KGs) are the key components of various natural language processing applications.
TuckER: Tensor Factorization for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts.
#2 best model for
Link Prediction
on FB15k-237
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.
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• TaoMiner/joint-kg-recommender
In this paper, we jointly learn the model of recommendation and knowledge graph completion.
Open-World Knowledge Graph Completion
• bxshi/ConMask
• 
Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking.
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).
DSKG: A Deep Sequential Model for Knowledge Graph Completion
• nju-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 $(subject, relation, object)$.
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence
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.
Expeditious Generation of Knowledge Graph Embeddings
Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space.
KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS LINK PREDICTION QUESTION ANSWERING