About: Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
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)
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)
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)
skos:notation
  • RIV/68407700:21230/07:03132304!RIV08-MSM-21230___
http://linked.open.../vavai/riv/strany
  • 798;805
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770038)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 421343
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/07:03132304
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • rule learning; semantic web (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [6094A96C1F84]
http://linked.open...v/mistoKonaniAkce
  • Varšava
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Machine Learning 2007
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Železný, Filip
  • Žáková, Monika
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-540-74957-8
http://localhost/t...ganizacniJednotka
  • 21230
is http://linked.open...avai/riv/vysledek of
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software