Knowledge Graph Completion Edit Task
Knowledge Base • Knowledge Graphs
About
Knowledge graphs $G$ are represented as a collection of triples $\{(h, r, t)\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
Benchmarks
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Subtasks
Datasets
Greatest papers with code
Knowledge Representation Learning: A Quantitative Review
• thunlp/OpenKE
• 
Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks.
INFORMATION RETRIEVAL KNOWLEDGE GRAPH COMPLETION LANGUAGE MODELLING QUESTION ANSWERING RECOMMENDATION SYSTEMS RELATION EXTRACTION REPRESENTATION LEARNING TRIPLE CLASSIFICATION
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.
Ranked #2 on
Knowledge Graphs
on FB15k
KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
• shaoxiongji/awesome-knowledge-graph
•
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPH EMBEDDING RELATIONAL REASONING
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).
Ranked #1 on
Knowledge Graph Completion
on FB15k-237
(Hits@1 metric)
KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPH EMBEDDINGS LINK PREDICTION
TuckER: Tensor Factorization for Knowledge Graph Completion
IJCNLP 2019
• Sujit-O/pykg2vec
•
Knowledge graphs are structured representations of real world facts.
Ranked #5 on
Link Prediction
on WN18
A survey of embedding models of entities and relationships for knowledge graph completion
• Sujit-O/pykg2vec
•
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 LINK PREDICTION
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.
KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
Ranked #3 on
Link Prediction
on FB15k-237
(MR metric)
KNOWLEDGE GRAPH COMPLETION LANGUAGE MODELLING LINK PREDICTION TRIPLE CLASSIFICATION
Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator.
Entity Context and Relational Paths for Knowledge Graph Completion
• muhanzhang/IGMC
•
Knowledge graph completion aims to predict missing relations between entities in a knowledge graph.