Science Citation Knowledge Extractor

Case ID:
UA17-116
Invention:

The Science Citation Knowledge Extractor (SCKE) uses natural language processing as well as machine learning to analyze biomedical research databases and extract key concepts from papers that are citing certain research. The Latent Semantic Analysis and Latent Dirichlet Allocation algorithms are used to do so, allowing researchers to see how the scientific community is using their research.

 

Background:

Not only are researchers interested in published scientific literature, they also want to know how their publications are being used in the broader scientific community. This software meets this need by using natural language processing to extract topics out of texts that cite a researcher's publication.

 

Applications:

  • Topic modeling
  • Patent search software

 

Advantages:

  • Ease of use
  • Employs natural language processing
  • Allows researchers to see how their research is being used by the community
Patent Information:
Contact For More Information:
Lewis Humphreys
Licensing Manager, Eller College of Mngmt & OTT
The University of Arizona
lewish@tla.arizona.edu
Lead Inventor(s):
Eric Lyons
Heather Lent
Keywords: