---
title: "View Learning for Statistical Relational Learning: With an Application to Mammography"
authors:
  - "Jesse Davis"
  - "Elizabeth S. Burnside"
  - "Inês de Castro Dutra"
  - "C. David Page Jr."
  - "Raghu Ramakrishnan"
  - "Vítor Santos Costa"
  - "Jude W. Shavlik"
doi: ""
url: "https://ijcai.org/proceedings/2005/62"
pdf: "https://ijcai.org/proceedings/2005/0062.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "677-682"
track: ""
keywords:
---

# View Learning for Statistical Relational Learning: With an Application to Mammography

Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, C. David Page Jr., Raghu Ramakrishnan, Vítor Santos Costa, Jude W. Shavlik

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 677-682.

## Abstract

Statistical relational learning (SRL) constructs probabilistic models from relational databases. A key capability of SRL is the learning of arcs (in the Bayes net sense) connecting entries in different rows of a relational table, or in different tables. Nevertheless, SRL approaches currently are constrained to use the existing database schema. For many database applications, users find it profitable to define alternative “views” of the database, in effect defining new fields or tables. Such new fields or tables can also be highly useful in learning. We provide SRL with the capability of learning new views.