Knowledge Graph Completion

218 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: $(h, r, ?)$.

Source: One-Shot Relational Learning for Knowledge Graphs


Use these libraries to find Knowledge Graph Completion models and implementations

Most implemented papers

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.

Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction


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

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

zjukg/kg-mm-survey 8 Feb 2024

In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.

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.

Multi-Relational Embedding for Knowledge Graph Representation and Analysis

tranhungnghiep/AnalyzeKGE PhD Dissertation, The Graduate University for Advanced Studies, SOKENDAI, Japan 2020

The goal of this thesis is first to study multi-relational embedding on knowledge graphs to propose a new embedding model that explains and improves previous methods, then to study the applications of multi-relational embedding in representation and analysis of knowledge graphs.

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


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