Learning Class Disjointness Axioms Using Grammatical Evolution
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gp-bibliography.bib Revision:1.7964
- @InProceedings{Nguyen:2019:EuroGP,
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author = "Thu Huong Nguyen and Andrea G. B. Tettamanzi",
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title = "Learning Class Disjointness Axioms Using Grammatical
Evolution",
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booktitle = "EuroGP 2019: Proceedings of the 22nd European
Conference on Genetic Programming",
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year = "2019",
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month = "24-26 " # apr,
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editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco",
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series = "LNCS",
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volume = "11451",
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publisher = "Springer Verlag",
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address = "Leipzig, Germany",
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pages = "278--294",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution: Poster",
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isbn13 = "978-3-030-16669-4",
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URL = "https://www.springer.com/us/book/9783030166694",
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DOI = "doi:10.1007/978-3-030-16670-0_18",
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size = "16 pages",
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abstract = "Today, with the development of the Semantic Web,
Linked Open Data (LOD), expressed using the Resource
Description Framework (RDF), has reached the status of
big data and can be considered as a giant data resource
from which knowledge can be discovered. The process of
learning knowledge defined in terms of OWL 2 axioms
from the RDF datasets can be viewed as a special case
of knowledge discovery from data or data mining, which
can be called RDF mining. The approaches to automated
generation of the axioms from recorded RDF facts on the
Web may be regarded as a case of inductive reasoning
and ontology learning. The instances, represented by
RDF triples, play the role of specific observations,
from which axioms can be extracted by generalization.
Based on the insight that discovering new knowledge is
essentially an evolutionary process, whereby hypotheses
are generated by some heuristic mechanism and then
tested against the available evidence, so that only the
best hypotheses survive, we propose the use of
Grammatical Evolution, one type of evolutionary
algorithm, for mining disjointness OWL 2 axioms from an
RDF data repository such as DBpedia. For the evaluation
of candidate axioms against the DBpedia dataset, we
adopt an approach based on possibility theory.",
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notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts
Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in
conjunction with EvoCOP2019, EvoMusArt2019 and
EvoApplications2019",
- }
Genetic Programming entries for
Thu Huong Nguyen
Andrea G B Tettamanzi
Citations