Knowledge Graph Completion

118 papers with code • 3 benchmarks • 9 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: $(h, r, ?)$.

Source: One-Shot Relational Learning for Knowledge Graphs

Libraries

Use these libraries to find Knowledge Graph Completion models and implementations

Most implemented papers

Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

MIRALab-USTC/KGE-HAKE 21 Nov 2019

HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.

Inductive Relation Prediction by Subgraph Reasoning

kkteru/grail ICML 2020

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.

TuckER: Tensor Factorization for Knowledge Graph Completion

ibalazevic/TuckER IJCNLP 2019

Knowledge graphs are structured representations of real world facts.

Relational Message Passing for Knowledge Graph Completion

hwwang55/PathCon 17 Feb 2020

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

RUCAIBox/UPGAN 28 Mar 2020

Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator.

A survey of embedding models of entities and relationships for knowledge graph completion

Sujit-O/pykg2vec 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.

Learning Sequence Encoders for Temporal Knowledge Graph Completion

nle-ml/mmkb EMNLP 2018

In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations.

KG-BERT: BERT for Knowledge Graph Completion

yao8839836/kg-bert 7 Sep 2019

Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.

Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion

MIRALab-USTC/KGE-DURA NeurIPS 2020

Tensor factorization based models have shown great power in knowledge graph completion (KGC).