Individuals, relations and structures in probabilistic models

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Relational data is equivalent to non-relational structured data. It is this equivalence which permits probabilistic models of relational data. Learning of probabilistic models for relational data is possible because one item of structured data is generally equivalent to many related data items. Succession and inclusion are two relations that have been well explored in the statistical literature. A description of the relevant statistical approaches is given. The representation of relational data via Bayesian nets is examined, and compared with PRMs. The paper ends with some cursory remarks on structured objects.
Original languageEnglish
Title of host publicationIJCAI Workshop on Learning Statistical Models from Relational Data (SRL2003)
EditorsLise Getoor, David Jensen
Number of pages5
Publication statusPublished - 1 Aug 2003

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