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
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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.
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
NAACL 2019
• daiquocnguyen/ConvKB
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In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object).
Open-World Knowledge Graph Completion
• bxshi/ConMask
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Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking.
DSKG: A Deep Sequential Model for Knowledge Graph Completion
• nju-websoft/DSKG
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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 $(subject, relation, object)$.
ProjE: Embedding Projection for Knowledge Graph Completion
• Sujit-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.
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.