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: $(h, r, ?)$.

Source: One-Shot Relational Learning for Knowledge Graphs

Greatest papers with code

Knowledge Representation Learning: A Quantitative Review

thunlp/OpenKE 28 Dec 2018

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.

General Classification Information Retrieval +7

Knowledge Graph Completion via Complex Tensor Factorization

Accenture/AmpliGraph 22 Feb 2017

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.

Knowledge Graph Completion Link Prediction +1

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

shaoxiongji/awesome-knowledge-graph 2 Feb 2020

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.

Knowledge Graph Completion Knowledge Graph Embedding +1

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

deepakn97/relationPrediction 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).

Knowledge Base Completion Knowledge Graph Completion +2

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

Sujit-O/pykg2vec 23 Mar 2017

Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.

Knowledge Base Completion Knowledge Graph Completion +1

ProjE: Embedding Projection for Knowledge Graph Completion

Sujit-O/pykg2vec 16 Nov 2016

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.

Fact Checking Feature Engineering +1

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.

Ranked #4 on Link Prediction on FB15k-237 (MR metric)

Knowledge Graph Completion Language Modelling +2

Relational Message Passing for Knowledge Graph Completion

muhanzhang/IGPL 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.

Knowledge Graph Completion

Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning

RUCDM/KB4Rec 28 Mar 2020

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

Knowledge Graph Completion