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Automatic Semantic Description Extraction from Social Big Data for Emergency Management
Bukhoree Sahoh1, Anant Choksuriwong1
1Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University, Songkla, Thailand.
Journal of Systems Science and Systems Engineering
|August 25, 2020
Summary
Social Big Data (SBD) provides crucial information for emergency management. This study introduces a semantic technique using triple store indexing for faster, more effective knowledge discovery from SBD to improve real-time emergency response.
Area of Science:
- Information Science
- Emergency Management
- Data Science
Background:
- Emergency events require rapid management for human safety and security.
- Social Big Data (SBD) from eyewitnesses is a valuable information source.
- Manual SBD analysis is slow and labor-intensive, hindering real-time emergency response.
Purpose of the Study:
- To develop an automatic knowledge discovery approach for SBD in emergency management.
- To propose a semantic description technique for efficient SBD information extraction.
Main Methods:
- Utilized triple store indexing for semantic description.
- Implemented named entity recognition and relation extraction.
- Focused on automatic knowledge discovery from Social Big Data.
Main Results:
- The proposed semantic technique effectively discovers hidden information in SBD.
- Achieved more efficient knowledge discovery compared to traditional methods.
- Demonstrated the potential for intelligent emergency management applications.
Conclusions:
- Automatic knowledge discovery from SBD is essential for effective emergency management.
- The semantic description technique offers a viable solution for real-time information extraction.
- This approach enhances the capabilities of intelligent emergency response systems.
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