Feature Interaction Convolutional Network for Knowledge Graph Embedding

2021 ◽  
pp. 369-380
Author(s):  
Jiachuan Li ◽  
Aimin Li ◽  
Teng Liu
Keyword(s):  
Graph Embedding ◽  
Feature Interaction ◽  
Knowledge Graph ◽  
Convolutional Network

End-to-end Relation-Enhanced Learnable Graph Self-attention Network for Knowledge Graphs Embedding

2021 ◽  
Author(s):  
Shengchen Jiang ◽  
Hongbin Wang ◽  
Xiang Hou
Keyword(s):  
Large Scale ◽  
Structural Characteristics ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Data Sets ◽  
Relevance Ranking ◽  
Convolutional Network ◽  
Attention Network ◽  
Knowledge Graphs ◽  
End To End

Abstract The existing methods ignore the adverse effect of knowledge graph incompleteness on knowledge graph embedding. In addition, the complexity and large-scale of knowledge information hinder knowledge graph embedding performance of the classic graph convolutional network. In this paper, we analyzed the structural characteristics of knowledge graph and the imbalance of knowledge information. Complex knowledge information requires that the model should have better learnability, rather than linearly weighted qualitative constraints, so the method of end-to-end relation-enhanced learnable graph self-attention network for knowledge graphs embedding is proposed. Firstly, we construct the relation-enhanced adjacency matrix to consider the incompleteness of the knowledge graph. Secondly, the graph self-attention network is employed to obtain the global encoding and relevance ranking of entity node information. Thirdly, we propose the concept of convolutional knowledge subgraph, it is constructed according to the entity relevance ranking. Finally, we improve the training effect of the convKB model by changing the construction of negative samples to obtain a better reliability score in the decoder. The experimental results based on the data sets FB15k-237 and WN18RR show that the proposed method facilitates more comprehensive representation of knowledge information than the existing methods, in terms of Hits@10 and MRR.

A Transition-Based Knowledge Graph Embedding with Adapting New Entities

2016 ◽  
Author(s):  
A-Yeong Kim ◽  
◽  
Hee-Guen Yoon ◽  
Seong-Bae Park ◽  
Se-Young Park ◽  
...  
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

A Knowledge Graph Embedding Approach for Metaphor Processing

2021 ◽  
Vol 29 ◽  
pp. 406-420
Author(s):  
Wei Song ◽  
Jingjin Guo ◽  
Ruiji Fu ◽  
Ting Liu ◽  
Lizhen Liu
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph ◽  
Metaphor Processing

scholarly journals MöbiusE: Knowledge Graph Embedding on Möbius ring

2021 ◽  
pp. 107181
Author(s):  
Yao Chen ◽  
Jiangang Liu ◽  
Zhe Zhang ◽  
Shiping Wen ◽  
Wenjun Xiong
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph

CyberEyes: Cybersecurity Entity Recognition Model Based on Graph Convolutional Network

2020 ◽  
Author(s):  
Yong Fang ◽  
Yuchi Zhang ◽  
Cheng Huang
Keyword(s):  
Complex Structure ◽  
Named Entity Recognition ◽  
Internet Technology ◽  
Entity Recognition ◽  
Local Context ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Evaluation Standard ◽  
Recognition Model ◽  
Non Local

Abstract Cybersecurity has gradually become the public focus between common people and countries with the high development of Internet technology in daily life. The cybersecurity knowledge analysis methods have achieved high evolution with the help of knowledge graph technology, especially a lot of threat intelligence information could be extracted with fine granularity. But named entity recognition (NER) is the primary task for constructing security knowledge graph. Traditional NER models are difficult to determine entities that have a complex structure in the field of cybersecurity, and it is difficult to capture non-local and non-sequential dependencies. In this paper, we propose a cybersecurity entity recognition model CyberEyes that uses non-local dependencies extracted by graph convolutional neural networks. The model can capture both local context and graph-level non-local dependencies. In the evaluation experiments, our model reached an F1 score of 90.28% on the cybersecurity corpus under the gold evaluation standard for NER, which performed better than the 86.49% obtained by the classic CNN-BiLSTM-CRF model.

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