Knowledge Graph Completion
206 papers with code • 7 benchmarks • 16 datasets
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
These leaderboards are used to track progress in Knowledge Graph Completion
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Libraries
Use these libraries to find Knowledge Graph Completion models and implementationsDatasets
MovieLens
FB15k
WN18
InferWiki
CoDEx Medium
Subtasks
Most implemented papers
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Knowledge graph completion aims to perform link prediction between entities.
ProjE: Embedding Projection for Knowledge Graph Completion
Sujit-O/pykg2vec
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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.
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.
A survey of embedding models of entities and relationships for knowledge graph completion
Sujit-O/pykg2vec
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COLING (TextGraphs) 2020
Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.
Joint Matrix-Tensor Factorization for Knowledge Base Inference
dair-iitd/kbi
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If not, what characteristics of a dataset determine the performance of MF and TF models?
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
daiquocnguyen/CapsE
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NAACL 2019
In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object).
Knowledge Representation Learning: A Quantitative Review
thunlp/OpenKE
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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.
Binarized Knowledge Graph Embeddings
This limitation is expected to become more stringent as existing knowledge graphs, which are already huge, keep steadily growing in scale.
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
deepakn97/relationPrediction
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ACL 2019
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).
Diachronic Embedding for Temporal Knowledge Graph Completion
In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time.
