Exploiting online social data in ontology learning for event tracking and emergency response

Authors:
Chung-Hong Lee;Chih-Hung Wu;Hsin-Chang Yang;Wei-Shiang Wen;Chang-Yi Chiang
Affiliations:
National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan;National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan;National University of Kaohsiung, Kaohsiung, Taiwan;National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan;National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

Citing 23
Cited 0

A study of retrospective and on-line event detection

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Automatic generation of overview timelines

SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
On computing correlated aggregates over continual data streams

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
DEMON: Mining and Monitoring Evolving Data

IEEE Transactions on Knowledge and Data Engineering
Learning Approaches for Detecting and Tracking News Events

IEEE Intelligent Systems
Ontology Learning for the Semantic Web

IEEE Intelligent Systems
Bursty and hierarchical structure in streams

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Ontologies for Knowledge Management: An Information Systems Perspective

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

Computational Linguistics
Adaptive Clustering for Multiple Evolving Streams

IEEE Transactions on Knowledge and Data Engineering
Using recursive ART network to construction domain ontology based on term frequency and inverse document frequency

Expert Systems with Applications: An International Journal
Evolutionary spectral clustering by incorporating temporal smoothness

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
StatStream: statistical monitoring of thousands of data streams in real time

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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
Tracking clusters in evolving data streams over sliding windows

Knowledge and Information Systems
Catching the Trend: A Framework for Clustering Concept-Drifting Categorical Data

IEEE Transactions on Knowledge and Data Engineering
Ontologies for crisis contagion management in financial institutions

Journal of Information Science
Text stream clustering algorithm based on adaptive feature selection

Expert Systems with Applications: An International Journal
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
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 paper, we describe our work on extracting entities from the online social messages regarding emergent events for ontology learning, which can contribute to a solution for quick response of emerging disastrous events. Our work started with the development of a real-time event detection system using a data-cluster slicing approach which combines social data analysis and early warning algorithms, allowing for quickly detecting emerging large-scale events from collected tweets. Subsequently, our system computes the energy of each collected event dataset, and then encapsulates ranked temporal, spatial and topical keywords into a structured node for event-entity extraction, in order to learn and update event ontologies for fast response of emergent events. The preliminary experimental results demonstrate that our developed system is workable, allowing for prediction of possible evolution for early warning of critical incidents with a dynamic ontology engineering.