Semantic Relation Extraction for Herb-Drug Interactions from the Biomedical Literature Using an Unsupervised Learning Approach

2018 ◽  
Author(s):  
Khang Trinh ◽  
Duy Pham ◽  
Ly Le
Keyword(s):  
Drug Interactions ◽  
Unsupervised Learning ◽  
Relation Extraction ◽  
Semantic Relation ◽  
Biomedical Literature ◽  
Learning Approach

scholarly journals Relation Extraction between Biomedical Entities from Literature using Semi- Supervised Learning Approach

2021 ◽  
Author(s):  
Saranya M ◽  
Arockia Xavier Annie R ◽  
Geetha T V
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Drug Repositioning ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Learning Approach ◽  
Computational Approaches ◽  
The World ◽  
The Cost ◽  
Very High

Now-a-days, people around the world are infected by many new diseases. The cost of developing or discovering a new drug for the newly discovered disease is very high and prolonged process. These could be eliminated with the help of already existing resources. To identify the candidates from the existing drugs, we need to extract the relation between the drug, target and disease by textming a large-scale literature. Recently, computational approaches which is used for identifying the relationships between the entities in biomedical domain are appearing as an active area of research for drug discovery as it needs more man power. Due to the limited computational approaches, the relation extraction between drug-gene and genedisease association from the unstructured biomedical documents is very hard. In this work, we proposed a semi-supervised approach named pattern based bootstrapping method to extract the direct relations between drug, gene and disease from the biomedical literature. These direct relationships are used to infer indirect relationships between entities such as drug and disease. Now these indirect relationships are used to determine the new candidates for drug repositioning which in turn will reduce the time and the patient’s risk.

scholarly journals Populating Web-Scale Knowledge Graphs Using Distantly Supervised Relation Extraction and Validation

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 316
Author(s):  
Sarthak Dash ◽  
Michael R. Glass ◽  
Alfio Gliozzo ◽  
Mustafa Canim ◽  
Gaetano Rossiello
Keyword(s):  
Deep Learning ◽  
Relation Extraction ◽  
Automated System ◽  
External Information ◽  
Learning Approach ◽  
Wide Margin ◽  
Structure Information ◽  
Adaptation Cost ◽  
Knowledge Graphs ◽  
Scale Experiment

In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. The designed system leverages a deep-learning-based technology for relation extraction that can be trained by a distantly supervised approach. In addition, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations. The designed system does not require any effort for adaptation to new languages and domains as it does not use any hand-labeled data, NLP analytics, and inference rules. Our experiments, performed on a popular academic benchmark, demonstrate that the suggested system boosts the performance of relation extraction by a wide margin, reporting error reductions of 50%, resulting in relative improvement of up to 100%. Furthermore, a web-scale experiment conducted to extend DBPedia with knowledge from Common Crawl shows that our system is not only scalable but also does not require any adaptation cost, while yielding a substantial accuracy gain.

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