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2017 | OriginalPaper | Chapter

Learning Commonalities in RDF

Authors : Sara El Hassad, François Goasdoué, Hélène Jaudoin

Published in: The Semantic Web

Publisher: Springer International Publishing

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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 (\(\mathtt {lgg}\)) 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 \(\mathtt {lgg}\)s of small-to-huge RDF graphs.

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previous chapter Updating Wikipedia via DBpedia Mappings and SPARQL
next chapter Lean Kernels in Description Logics
Footnotes
1
The merge of RDF graphs is their union after renaming their blank nodes, so that these RDF graphs do not join on such values which are local to them (Sect. 2).
 
2
We assume w.l.o.g. that input and output data of an MR job is stored on disk, like in Hadoop, while it can also reside in in-memory shared data structures, like in Spark.
 
3
An \(\mathcal {EL}\) concept C recursively translates into the RDF graph rooted in the blank node \(b_r\) returned by the call \(\mathcal {G}(C,b_r)\), with: \(\mathcal {G}(\top ,b)=\emptyset \) for the universal \(\mathcal {EL}\) concept \(\top \), \(\mathcal {G}(A,b)=\{(b,\tau ,A)\}\) for an atomic \(\mathcal {EL}\) concept A, \(\mathcal {G}(\exists r.C,b)=\{(b,r,b')\} \cup \mathcal {G}(C,b')\), with \(b'\) a fresh blank node, for an \(\mathcal {EL}\) existential restriction \(\exists r.C\), and \(\mathcal {G}(C_1 \sqcap C_2,b)= \mathcal {G}(C_1,b)\cup \mathcal {G}(C_2,b)\) for an \(\mathcal {EL}\) conjunction \(C_1 \sqcap C_2\); the \(\mathcal {EL}\) constraints \(A_1 \sqsubseteq A_2\) and \(\exists r.\top \sqsubseteq A\) correspond to \((A_1,\preceq _\mathrm {sc},A_2)\) and \((r,\hookleftarrow _d,A)\) resp.
 
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Metadata
Title
Learning Commonalities in RDF
Authors
Sara El Hassad
François Goasdoué
Hélène Jaudoin
Copyright Year
2017
Book
The Semantic Web

Print ISBN: 978-3-319-58067-8

Electronic ISBN: 978-3-319-58068-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-58068-5_31