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Description
| - Reprezentace znalostí využívané v oblasti sémantického webu často obsahují informace, které je možno převést do formy taxonomií na termech a predikátech v relačním učení. V tomto článku ukazujeme, jak je možné řádově zrychlit propozicionalizaci relačních dat s využitím nového operátoru specializace, který využívá zmíněné taxonomie. Dále ukazujeme, jak je možné zrychlit proces následného propozicionálního učení tím, že propozicionálním algoritmu předáme informaci o taxonomii na rysech, které jsou výsledkem propozicionalizace a slouží jako vstupní data pro propozicionální algoritmus. (cs)
- Knowledge representations using semantic web technologies often provide information which translates to explicit term and predicate taxonomies in relational learning. Here we show how to speed up the process of propositionalization of relational data by orders of magnitude, by exploiting such ontologies through a novel refinement operator used in the construction of conjunctive relational features. Moreover, we accelerate the subsequent search conducted by a propositional learning algorithm by providing it with information on feature generality taxonomy, determined from the initial term and predicate taxonomies but also accounting for traditional $\theta$ subsumption between features. This information enables the propositional rule learner to prevent the exploration of useless conjunctions containing a feature together with any of its subsumees and to specialize a rule by replacing a feature by its subsumee.
- Knowledge representations using semantic web technologies often provide information which translates to explicit term and predicate taxonomies in relational learning. Here we show how to speed up the process of propositionalization of relational data by orders of magnitude, by exploiting such ontologies through a novel refinement operator used in the construction of conjunctive relational features. Moreover, we accelerate the subsequent search conducted by a propositional learning algorithm by providing it with information on feature generality taxonomy, determined from the initial term and predicate taxonomies but also accounting for traditional $\theta$ subsumption between features. This information enables the propositional rule learner to prevent the exploration of useless conjunctions containing a feature together with any of its subsumees and to specialize a rule by replacing a feature by its subsumee. (en)
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Title
| - Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning
- Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning (en)
- Využití taxonomií na termech, predikátech a rysech v propozicionalizaci a učení propozicionálních pravidel (cs)
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skos:prefLabel
| - Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning
- Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning (en)
- Využití taxonomií na termech, predikátech a rysech v propozicionalizaci a učení propozicionálních pravidel (cs)
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skos:notation
| - RIV/68407700:21230/07:03132304!RIV08-MSM-21230___
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http://linked.open.../vavai/riv/strany
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/07:03132304
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - rule learning; semantic web (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Železný, Filip
- Žáková, Monika
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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http://linked.open...n/vavai/riv/zamer
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number of pages
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http://purl.org/ne...btex#hasPublisher
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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is http://linked.open...avai/riv/vysledek
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