DocumentCode :
1825938
Title :
Exploiting online social data in ontology learning for event tracking and emergency response
Author :
Chung-Hong Lee ; Chih-Hung Wu ; Hsin-Chang Yang ; Wei-Shiang Wen ; Chang-Yi Chiang
Author_Institution :
Dept. of Electr. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan
fYear :
2013
fDate :
25-28 Aug. 2013
Firstpage :
1167
Lastpage :
1174
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.
Keywords :
alarm systems; data mining; disasters; emergency management; learning (artificial intelligence); ontologies (artificial intelligence); social networking (online); critical incidents; data-cluster slicing approach; disastrous events; dynamic ontology engineering; early warning algorithms; emergency response; emergent events; emerging large-scale event detection; entity extraction; event dataset collection; event ontology update; event tracking; event-entity extraction; event-entity response; online social data; online social messages; ontology learning; ranked spatial keyword encapsulation; ranked temporal keyword encapsulation; ranked topical keyword encapsulation; real-time event detection system; social data analysis; structured node; tweet collection; Data mining; Equations; Event detection; Message systems; Ontologies; Real-time systems; Social network services; emergency response; event detection; ontology; social mining; stream mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
Conference_Location :
Niagara Falls, ON
Type :
conf
Filename :
6785851
Link To Document :
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