Knowledge Graph Completion
76 papers with code • 3 benchmarks • 3 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
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MovieLens
FB15k
CoDEx
Greatest papers with code
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.
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
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
shaoxiongji/awesome-knowledge-graph
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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.
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).
Ranked #1 on
Knowledge Graph Completion
on FB15k-237
TuckER: Tensor Factorization for Knowledge Graph Completion
Sujit-O/pykg2vec
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IJCNLP 2019
Knowledge graphs are structured representations of real world facts.
Ranked #7 on
Link Prediction
on FB15k
A survey of embedding models of entities and relationships for knowledge graph completion
Sujit-O/pykg2vec
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Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.
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.
KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
Ranked #4 on
Link Prediction
on FB15k-237
(MR metric)
Relational Message Passing for Knowledge Graph Completion
muhanzhang/IGPL
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Specifically, two kinds of neighborhood topology are modeled for a given entity pair under the relational message passing framework: (1) Relational context, which captures the relation types of edges adjacent to the given entity pair; (2) Relational paths, which characterize the relative position between the given two entities in the knowledge graph.
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.