Document Details

Document Type : Thesis 
Document Title :
Big Data Knowledge Mining
التنقيب في البيانات الكبيره
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : The era of Big Data (BD) has arrived. The rise of big data applications where data collection has grown beyond the capability of the current software tool to capture, manage and process within tolerable elapsed time. Volume is not the only the characteristic that defines big data, but also velocity, variety, and value. Many resources generate BD that should be processed. The biomedical research literature is one among many other domains that hides rich knowledge. MEDLINE is a huge database of biomedical research papers which remain a significantly underutilized source of biological information. Discovering the useful knowledge from such huge corpus leads to various problems related to the type of information such as the concepts related to the domain of texts and the semantic relationship associated with them. In this paper, we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified Medical Language System (UMLS) Knowledgebase. The model uses a Self-supervised Approach for Relation Extraction (RE) by constructing enhanced training examples using information from UMLS and incorporates Spark BD technology with multiple Data Mining and machine learning technique with Multi Agent System (MAS). The system shows a better result in comparison with the current state of the art and naïve approach in terms of Accuracy, Precision, Recall and F-score. 
Supervisor : Dr. Kamal Mansour Jambi 
Thesis Type : Master Thesis 
Publishing Year : 1438 AH
2016 AD 
Co-Supervisor : DR. Mason F. Abulkhair 
Added Date : Wednesday, January 11, 2017 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
هـــــدى عــــمر بانقيطهBanuqitah, Huda UmarResearcherMaster 

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