Learning Commonalities in RDF

Abstract : 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 Knowlegge 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.
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/hal-01485862
Contributor : François Goasdoué <>
Submitted on : Thursday, March 9, 2017 - 2:41:38 PM
Last modification on : Thursday, November 15, 2018 - 11:58:50 AM
Long-term archiving on : Saturday, June 10, 2017 - 2:35:38 PM

File

paper.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01485862, version 1

Citation

Sara El Hassad, François Goasdoué, Hélène Jaudoin. Learning Commonalities in RDF. Extended Semantic Web Conference (ESWC), May 2017, Portoroz, Slovenia. ⟨hal-01485862v1⟩

Share

Metrics

Record views

259

Files downloads

49