Abstract
Relational data is equivalent to non-relational struc-
tured data. It is this equivalence which permits
probabilistic models of relational data. Learning
of probabilistic models for relational data is possi-
ble 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 pa-
per ends with some cursory remarks on structured
objects.
tured data. It is this equivalence which permits
probabilistic models of relational data. Learning
of probabilistic models for relational data is possi-
ble 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 pa-
per ends with some cursory remarks on structured
objects.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IJCAI 2003 Workshop on Learning Statistical Models from Relational Data (SRL 2003) |
| Pages | 32-36 |
| Publication status | Published - 2003 |