A Multi-Hop Link Prediction Approach Based on Reinforcement Learning in Knowledge Graphs

2018 ◽  
Author(s):  
Heng Chen ◽  
Guanyu Li ◽  
Yunhao Sun ◽  
Wei Jiang ◽  
Xinying Chen
Keyword(s):  
Reinforcement Learning ◽  
Link Prediction ◽  
Knowledge Graphs ◽  
Prediction Approach

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.

scholarly journals Trans4E: Link Prediction on Scholarly Knowledge Graphs

2021 ◽  
Author(s):  
Mojtaba Nayyeri ◽  
Gokce Muge Cil ◽  
Sahar Vahdati ◽  
Francesco Osborne ◽  
Mahfuzur Rahman ◽  
...  
Keyword(s):  
Link Prediction ◽  
Knowledge Graphs

Estimating potential trade links in the international crude oil trade: A link prediction approach

Energy ◽  
2016 ◽  
Vol 102 ◽  
pp. 406-415 ◽  
Author(s):  
Qing Guan ◽  
Haizhong An ◽  
Xiangyun Gao ◽  
Shupei Huang ◽  
Huajiao Li
Keyword(s):  
Crude Oil ◽  
Link Prediction ◽  
Prediction Approach

scholarly journals Hidden Link Prediction in Criminal Networks Using the Deep Reinforcement Learning Technique

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Marcus Lim ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Network Analysis ◽  
Prediction Model ◽  
Supervised Learning ◽  
Link Prediction ◽  
Supervised Machine Learning ◽  
Criminal Networks ◽  
Criminal Network ◽  
Learning Technique

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

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