Difference between revisions of "Continuous Semantics to Analyze Real Time Data"

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<span style="font-size:28pt;color:purple">Continuous Semantics to Analyze Real-Time Data</span><br /><br />
 
<span style="font-size:28pt;color:purple">Continuous Semantics to Analyze Real-Time Data</span><br /><br />
Amit Sheth, Christopher Thomas, and Pankaj Mehra • <i>Wright State University</i><br />
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Amit Sheth, Christopher Thomas, and Pankaj Mehra • <i>Wright State University</i><br /><br />
  
<span style="font-size:28pt;color:purple">W</span>e’ve made significant progress in
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<span style="font-size:24pt;color:purple">W</span>e’ve made significant progress in
 
applying semantics and Semantic Web
 
applying semantics and Semantic Web
 
technologies in a range of domains. A
 
technologies in a range of domains. A
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mobile, and sensor webs. Here, we look at how
 
mobile, and sensor webs. Here, we look at how
 
continuous semantics can help us model those
 
continuous semantics can help us model those
domains and analyze the related real-time data.
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domains and analyze the related real-time data.<br />
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<span style="font-size:12pt;color:purple">The Challenge of Modeling Dynamic Domains</span>

Revision as of 15:52, 4 October 2010

Continuous Semantics to Analyze Real-Time Data

Amit Sheth, Christopher Thomas, and Pankaj Mehra • Wright State University

We’ve made significant progress in applying semantics and Semantic Web technologies in a range of domains. A relatively well-understood approach to reaping semantics’ benefits begins with formal modeling of a domain’s concepts and relationships, typically as an ontology. Then, we extract relevant facts — in the form of related entities — from the corpus of background knowledge and use them to populate the ontology. Finally, we apply the ontology to extract semantic metadata or to semantically annotate data in unseen or new corpora. Using annotations yields semanticsenhanced experiences for search, browsing, integration, personalization, advertising, analysis, discovery, situational awareness, and so on.1 This typically works well for domains that involve slowly evolving knowledge concentrated among deeply specialized domain experts and that have definable boundaries. A good example is the US National Center for Biomedical Ontologies, which has approximately 200 ontologies used for annotations, improved search, reasoning, and knowledge discovery. Concurrently, major search engines are developing and using large collections of domain-relevant entities as background knowledge, to support semantic or facet search. However, this approach has difficulties dealing with dynamic domains involved in social, mobile, and sensor webs. Here, we look at how continuous semantics can help us model those domains and analyze the related real-time data.

The Challenge of Modeling Dynamic Domains