Main Page
Deanship
The Dean
Dean's Word
Curriculum Vitae
Contact the Dean
Vision and Mission
Organizational Structure
Vice- Deanship
Vice- Dean
KAU Graduate Studies
Research Services & Courses
Research Services Unit
Important Research for Society
Deanship's Services
FAQs
Research
Staff Directory
Files
Favorite Websites
Deanship Access Map
Graduate Studies Awards
Deanship's Staff
Staff Directory
Files
Researches
Contact us
عربي
English
About
Admission
Academic
Research and Innovations
University Life
E-Services
Search
Deanship of Graduate Studies
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 Type
Dr Grade
Email
هـــــدى عــــمر بانقيطه
Banuqitah, Huda Umar
Researcher
Master
Files
File Name
Type
Description
39572.pdf
pdf
Back To Researches Page