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# Knowledge Graph Completion Edit

18 papers with code · Knowledge Base

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# NoiGAN: NOISE AWARE KNOWLEDGE GRAPH EMBEDDING WITH GAN

Knowledge graph has gained increasing attention to recent years for its successful applications of numerous tasks.

# Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding

9 Oct 2019

The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces.

# Representation Learning with Ordered Relation Paths for Knowledge Graph Completion

26 Sep 2019

Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning.

# Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

25 Sep 2019

Therefore, we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions.

# Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle

19 Sep 2019

In many such scenarios the base text, from which the knowledge graph is constructed, concerns itself with practical, on-hand, actual or ground-reality information about the domain.

# Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space

17 Sep 2019

The knowledge graph can be modeled by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between these embedding vectors.

# Combination of Unified Embedding Model and Observed Features for Knowledge Graph Completion

9 Sep 2019

Then, we show that these models utilize paths for link prediction and propose a method for evaluating rules based on this idea.

# Composing Knowledge Graph Embeddings via Word Embeddings

9 Sep 2019

As $(\mathbf{h},\mathbf{r},\mathbf{t})$ is learned from the existing facts within a knowledge graph, these representations can not be used to detect unknown facts (if the entities or relations never occur in the knowledge graph).

# Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding

2 Sep 2019

We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.

# Diachronic Embedding for Temporal Knowledge Graph Completion

6 Jul 2019

In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time.