# Knowledge Graph Completion

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

## Libraries

Use these libraries to find Knowledge Graph Completion models and implementations## Datasets

## Subtasks

## Most implemented papers

# 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.

# Inductive Relation Prediction by Subgraph Reasoning

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

Knowledge graphs are structured representations of real world facts.

# Relational Message Passing for Knowledge Graph Completion

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.

# Multi-Relational Embedding for Knowledge Graph Representation and Analysis

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.

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

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

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

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).