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Extracting Entities of Emergent Events from Social Streams Based on a Data-Cluster Slicing Approach for Ontology Engineering

Extracting Entities of Emergent Events from Social Streams Based on a Data-Cluster Slicing Approach for Ontology Engineering

Chung-Hong Lee, Chih-Hung Wu
Copyright: © 2015 |Volume: 5 |Issue: 3 |Pages: 18
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781466679238|DOI: 10.4018/IJIRR.2015070101
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MLA

Lee, Chung-Hong, and Chih-Hung Wu. "Extracting Entities of Emergent Events from Social Streams Based on a Data-Cluster Slicing Approach for Ontology Engineering." IJIRR vol.5, no.3 2015: pp.1-18. http://doi.org/10.4018/IJIRR.2015070101

APA

Lee, C. & Wu, C. (2015). Extracting Entities of Emergent Events from Social Streams Based on a Data-Cluster Slicing Approach for Ontology Engineering. International Journal of Information Retrieval Research (IJIRR), 5(3), 1-18. http://doi.org/10.4018/IJIRR.2015070101

Chicago

Lee, Chung-Hong, and Chih-Hung Wu. "Extracting Entities of Emergent Events from Social Streams Based on a Data-Cluster Slicing Approach for Ontology Engineering," International Journal of Information Retrieval Research (IJIRR) 5, no.3: 1-18. http://doi.org/10.4018/IJIRR.2015070101

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Abstract

In this paper, we describe our work on developing a model and method for extracting key entities from the online social messages regarding emergent events for enhancing ontology engineering, enabling a sensible solution for prevention of similar disasters. Our work started with the development of an event modelling system using a data-cluster slicing approach, which combines analytics of social data and event lifecycle algorithms, allowing for large-scale emerging novel events to be quickly and accurately analyzed. Subsequently, our system computes the energy of each collected event data sets, and then encapsulates ranked temporal, spatial and topical keywords into a structured node for event-entity extraction, in order to 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 and early warning of critical incidents with a support of dynamic entity extraction.

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