Learning Commonalities in RDF
published: July 10, 2017, recorded: May 2017, views: 4
Report a problem or upload filesIf 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.
Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning introduced in the 70’s, which amounts to computing a least general generalization (lgg) of such descriptions. It has also started receiving consideration in Knowledge Representation from the 90’s, and recently in the Semantic Web field. We revisit this problem in the popular Resource Description Framework (RDF) of W3C, where descriptions are RDF graphs, i.e., a mix of data and knowledge. Notably, and in contrast to the literature, our solution to this problem holds for the entire RDF standard, i.e., we do not restrict RDF graphs in any way (neither their structure nor their semantics based on RDF entailment, i.e., inference) and, further, our algorithms can compute lggs of small-to-huge RDF graphs.
Download slides: eswc2017_el_hassad_learning_commonalities_01.pdf (660.9 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !