Probabilistic Inductive Querying Using ProbLog

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dc.contributor University of Helsinki, Finnish Centre of Excellence in Algorithmic Data Analysis Research (Algodan) en De Raedt, Luc Kimmig, Angelika Gutmann, Bernd Kersting, Kristian Santos Costa, Vitor Toivonen, Hannu
dc.contributor.editor Dzeroski, Saso
dc.contributor.editor Goethals, Bart
dc.contributor.editor Panov, Pance 2011-02-03T07:45:01Z 2011-02-03T07:45:01Z 2010
dc.identifier.citation De Raedt , L , Kimmig , A , Gutmann , B , Kersting , K , Santos Costa , V & Toivonen , H 2010 , Probabilistic Inductive Querying Using ProbLog . in S Dzeroski , B Goethals & P Panov (eds) , Inductive Databases and Constraint-Based Data Mining . Springer , pp. 229-262 . en
dc.identifier.isbn 978-1-4419-7737-3
dc.identifier.isbn 978-1-4419-7738-0
dc.identifier.other PURE: 13092376
dc.identifier.other PURE UUID: 9578a6e3-c700-4c45-8b49-0e6fc7b56a40
dc.identifier.other Scopus: 79955777896
dc.identifier.other ORCID: /0000-0003-1339-8022/work/29478237
dc.description.abstract We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog. ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms. After a short introduction to ProbLog, we provide a survey of the different types of inductive queries that ProbLog supports, and show how it can be applied to the mining of large biological networks. en
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Inductive Databases and Constraint-Based Data Mining
dc.rights en
dc.subject 113 Computer and information sciences en
dc.title Probabilistic Inductive Querying Using ProbLog en
dc.type Chapter
dc.type.uri info:eu-repo/semantics/other

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