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Computational Cognitive Model for Semantic Sub-network Extraction from Natural Language Queries

Search engines enable us to locate information from a pool of unorganized resources.  They interpret the input of key words and topics and try to produce the correct material. Current search engines require the user to develop keywords to effectively locate the needed information.  

Researchers at the University of Missouri have developed a search engine that understands how humans work. Their novel technique leverages cognitive psychology for semantic sub-network extraction from natural language queries.  These cognitive psychology notions are one of the fundamental structures employed by human cognition to construct semantic information in the brain.  Utilizing these notions to interpret inputs from users will increase the accuracy and efficiency of search engines, changing how users access information. 

 

POTENTIAL AREAS OF APPLICATIONS:
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Search engines
-Web search
-Networked intelligence applications and services

 MAIN ADVANTAGES OF INVENTION:
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Improve parsing and sub-network extraction from natural language queries

STATE OF DEVELOPMENT:
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Fully functional

Additional Details

Owner

University of Missouri - Columbia

Intellectual Property Protection

Pending Patent



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