Learning to create an extensible event ontology model from social-media streams

Authors:
Chung-Hong Lee;Chih-Hung Wu;Hsin-Chang Yang;Wei-Shiang Wen
Affiliations:
Dept of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan;Dept of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan;Dept of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan;Dept of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

Citing 11
Cited 0

Ontology Learning for the Semantic Web

IEEE Intelligent Systems
Ontologies for Knowledge Management: An Information Systems Perspective

Knowledge and Information Systems
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites

Computational Linguistics
Using recursive ART network to construction domain ontology based on term frequency and inverse document frequency

Expert Systems with Applications: An International Journal
TextOntoEx: Automatic ontology construction from natural English text

Expert Systems with Applications: An International Journal
A flexible framework to experiment with ontology learning techniques

Knowledge-Based Systems
Ontologies for crisis contagion management in financial institutions

Journal of Information Science
Knowledge level modeling for systemic risk management in financial institutions

Expert Systems with Applications: An International Journal
BursT: a dynamic term weighting scheme for mining microblogging messages

ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Text2Onto: a framework for ontology learning and data-driven change discovery

NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Mining spatio-temporal information on microblogging streams using a density-based online clustering method

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work we utilize the social messages to construct an extensible event ontology model for learning the experiences and knowledge to cope with emerging real-world events. We develop a platform combining several text mining and social analysis algorithms to cooperate with our stream mining approach to detecting large-scale disastrous events from social messages, in order to achieve the aim of automatically constructing event ontology for emergency response First, we employ the developed event detection technique on Twitter social-messages to monitor the occurrence of emerging events, and record the development and evolution of detected events. Furthermore, we store the messages associated with the detected events in a repository. Through the developed algorithms for analyzing the content of social messages and ontology construction the event ontology can be established, allowing for developing relevant applications for prediction of possible evolution and impact evaluation of the events in the future immediately, in order to achieve the goals for early warning of disasters and risk management.