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This ontology document is licensed under the Creative Commons
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	<pub:InProceedings rdf:about="http://ebiquity.umbc.edu/paper/html/id/777/A-Deep-Learning-Approach-to-Understanding-Cloud-Service-Level-Agreements-">
		<rdfs:label><![CDATA[A Deep Learning Approach to Understanding Cloud Service Level Agreements	]]></rdfs:label>
		<pub:title><![CDATA[A Deep Learning Approach to Understanding Cloud Service Level Agreements	]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2017-05-24T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[Educational organizations, like Universities and School Systems, are rapidly adopting Cloud based services to provide Information Technology (IT) infrastructure to their students. These include course offerings, class materials, ​data storage, emailing and collaboration software, virtual computing environment, etc. Moreover, cloud providers, like Amazon, are also providing free computing credits targeted to students. The legal documents associated with cloud based
services, such as Service Level Agreements (SLAs), provide information regarding quality and use of cloud services. These documents are often long text-based documents containing domain specific terminology. In addition, this terminology varies from one document or service provider to another. We propose a framework to extract semantically similar terms and entities across cloud service documents using word embeddings and neural networks. Our work is
intended to aid cloud service consumers across a variety of fields by providing the ability to
understand the services and requirements offered by large-scale commercial cloud services. In some of our previous papers, we have used semantic web and natural language processing to
analyze SLA and privacy policy documents for cloud services [1, 2, 3]. In this work, we extend
our approach to propose a deep learning-based technique to analyze these documents and
populate cloud service ontologies. The preliminary analysis of cloud SLAs documents performed by us showed that deep learning techniques are useful in context disambiguation and identifying semantically similar terminology across services.
]]></pub:abstract>
		<pub:organization><![CDATA[IBM]]></pub:organization>
		<pub:counter>1748</pub:counter>
		<pub:tag><![CDATA[cloud service]]></pub:tag>
		<pub:tag><![CDATA[deep learning]]></pub:tag>
		<pub:tag><![CDATA[natural language processing]]></pub:tag>
		<pub:tag><![CDATA[word embedding]]></pub:tag>
		<pub:booktitle><![CDATA[Fifth International IBM Cloud Academy Conference]]></pub:booktitle>
		<pub:author>
			<rdf:List>
				<rdf:first>
					<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Karuna/Joshi"><person:name><![CDATA[Karuna Pande Joshi]]></person:name><rdfs:label><![CDATA[Karuna Pande Joshi]]></rdfs:label></person:Person>
				</rdf:first>
				<rdf:rest>					<rdf:List>
						<rdf:first>
							<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Aditi/Gupta"><person:name><![CDATA[Aditi Gupta]]></person:name><rdfs:label><![CDATA[Aditi Gupta]]></rdfs:label></person:Person>
						</rdf:first>
						<rdf:rest>							<rdf:List>
								<rdf:first>
									<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Srishty/Saha"><person:name><![CDATA[Srishty Saha]]></person:name><rdfs:label><![CDATA[Srishty Saha]]></rdfs:label></person:Person>
								</rdf:first>
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		</pub:author>
		<pub:firstAuthor>
<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Karuna/Joshi"><person:name><![CDATA[Karuna Pande Joshi]]></person:name><rdfs:label><![CDATA[Karuna Pande Joshi]]></rdfs:label></person:Person>
		</pub:firstAuthor>
		<pub:relatedProject><project:PastProject rdf:about="http://ebiquity.umbc.edu/project/html/id/105/ALDA-Automated-Legal-Document-Analytics"><project:title><![CDATA[ALDA: Automated Legal Document Analytics]]></project:title><rdfs:label><![CDATA[ALDA: Automated Legal Document Analytics]]></rdfs:label></project:PastProject></pub:relatedProject>
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			<pub:softCopyFormat><![CDATA[PDF Document]]></pub:softCopyFormat>
			<pub:softCopyURI><![CDATA[http://ebiquity.umbc.edu/get/a/publication/848.pdf]]></pub:softCopyURI>
			<pub:softCopySize>139744</pub:softCopySize>
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	</pub:InProceedings>

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