Mobile-Based Emergency Response System Using Ontology-Supported Information Extraction

Amailef, K; Lu, J

Mobile-Based Emergency Response System Using Ontology-Supported Information Extraction

Amailef, K Lu, J

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Publication Type:: Chapter
Citation:: 2012, 33 pp. 429 - 449
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Amailef, K	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	2012, 33 pp. 429 - 449	en_US
dc.identifier.isbn	9783642257544	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22836
dc.description.abstract	This chapter describes an algorithm within a Mobile-based Emergency Response System (MERS) to automatically extract information from Short Message Service (SMS). The algorithm is based on an ontology concept, and a maximum entropy statistical model. Ontology has been used to improve the performance of an information extraction system. A maximum entropy statistical model with various predefined features offers a clean way to estimate the probability of certain token occurring with a certain SMS text. The algorithm has four main functions: to collect unstructured information from an SMS emergency text message; to conduct information extraction and aggregation; to calculate the similarity of SMS text messages; and to generate query and results presentation. © Springer-Verlag Berlin Heidelberg 2012.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0880739
dc.relation.isbasedon	10.1007/978-3-642-25755-1_21	en_US
dc.title	Mobile-Based Emergency Response System Using Ontology-Supported Information Extraction	en_US
dc.type	Chapter
utslib.citation.volume	33	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

This chapter describes an algorithm within a Mobile-based Emergency Response System (MERS) to automatically extract information from Short Message Service (SMS). The algorithm is based on an ontology concept, and a maximum entropy statistical model. Ontology has been used to improve the performance of an information extraction system. A maximum entropy statistical model with various predefined features offers a clean way to estimate the probability of certain token occurring with a certain SMS text. The algorithm has four main functions: to collect unstructured information from an SMS emergency text message; to conduct information extraction and aggregation; to calculate the similarity of SMS text messages; and to generate query and results presentation. © Springer-Verlag Berlin Heidelberg 2012.

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http://hdl.handle.net/10453/22836