# Knowledge Graph Completion   Edit

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: $(h, r, ?)$.

# Knowledge Representation Learning: A Quantitative Review

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.

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# Knowledge Graph Completion via Complex Tensor Factorization

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.

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# A Survey on Knowledge Graphs: Representation, Acquisition and Applications

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.

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# Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

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).

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# TuckER: Tensor Factorization for Knowledge Graph Completion

Knowledge graphs are structured representations of real world facts.

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

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.

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# ProjE: Embedding Projection for Knowledge Graph Completion

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.

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# KG-BERT: BERT for Knowledge Graph Completion

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)

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# Relational Message Passing for Knowledge Graph Completion

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.

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

28 Mar 2020

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

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