Statistical Learning for Inductive Query Answering on OWL Ontologies
published: Nov. 24, 2008, recorded: October 2008, views: 2709
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
A novel family of parametric language-independent kernel functions defined for individuals within ontologies is presented. They are easily integrated with efficient statistical learning methods for inducing linear classifiers that offer an alternative way to perform classification w.r.t. deductive reasoning. A method for adapting the parameters of the kernel to the knowledge base through stochastic optimization is also proposed. This enables the exploitation of statistical learning in a variety of tasks where an inductive approach may bridge the gaps of the standard methods due the inherent incompleteness of the knowledge bases. In this work, a system integrating the kernels has been tested in experiments on approximate query answering with real ontologies collected from standard repositories.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: