Cover Image

Enhanced verbalization of ORM models

Fact-oriented modeling approaches such as Object-Role Modeling (ORM) validate their models with domain experts by verbalizing the models in natural language, and by populating the relevant fact types with concrete examples. This paper extends previous work on verbalization of ORM models in a number...

Full description

Saved in:
Bibliographic Details
Main Authors: Curland, Matthew, Halpin, Terry
Format: Teaching Resource
Language:English
Published: Springer Berlin Heidelberg 2012
Subjects:
QA76 Computer software
Online Access:http://eprints.intimal.edu.my/128/1/11.pdf
http://eprints.intimal.edu.my/128/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-inti-eprints.128
record_format eprints
spelling my-inti-eprints.1282016-04-06T10:13:54Z http://eprints.intimal.edu.my/128/ Enhanced verbalization of ORM models Curland, Matthew Halpin, Terry QA76 Computer software Fact-oriented modeling approaches such as Object-Role Modeling (ORM) validate their models with domain experts by verbalizing the models in natural language, and by populating the relevant fact types with concrete examples. This paper extends previous work on verbalization of ORM models in a number of ways. Firstly, it considers some ways to better ensure that generated verbalizations are unambiguous, including occasional use of lengthier verbalizations that are tied more closely to the underlying logical form. Secondly, it provides improved verbalization patterns for common types of ORM constraints, such as uniqueness and mandatory role constraints. Thirdly, it provides an algorithm for verbalizing external uniqueness and frequency constraints over roles projected from join paths of arbitrary complexity. The paper also includes some discussion of how such verbalization enhancements were recently implemented in the Natural ORM Architect (NORMA) tool. Springer Berlin Heidelberg 2012 Teaching Resource NonPeerReviewed text en http://eprints.intimal.edu.my/128/1/11.pdf Curland, Matthew and Halpin, Terry (2012) Enhanced verbalization of ORM models. [Teaching Resource]
institution INTI International University
building INTI Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider INTI International University
content_source INTI Institutional Repository
url_provider http://eprints.intimal.edu.my
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Curland, Matthew
Halpin, Terry
Enhanced verbalization of ORM models
description Fact-oriented modeling approaches such as Object-Role Modeling (ORM) validate their models with domain experts by verbalizing the models in natural language, and by populating the relevant fact types with concrete examples. This paper extends previous work on verbalization of ORM models in a number of ways. Firstly, it considers some ways to better ensure that generated verbalizations are unambiguous, including occasional use of lengthier verbalizations that are tied more closely to the underlying logical form. Secondly, it provides improved verbalization patterns for common types of ORM constraints, such as uniqueness and mandatory role constraints. Thirdly, it provides an algorithm for verbalizing external uniqueness and frequency constraints over roles projected from join paths of arbitrary complexity. The paper also includes some discussion of how such verbalization enhancements were recently implemented in the Natural ORM Architect (NORMA) tool.
format Teaching Resource
author Curland, Matthew
Halpin, Terry
author_facet Curland, Matthew
Halpin, Terry
author_sort Curland, Matthew
title Enhanced verbalization of ORM models
title_short Enhanced verbalization of ORM models
title_full Enhanced verbalization of ORM models
title_fullStr Enhanced verbalization of ORM models
title_full_unstemmed Enhanced verbalization of ORM models
title_sort enhanced verbalization of orm models
publisher Springer Berlin Heidelberg
publishDate 2012
url http://eprints.intimal.edu.my/128/1/11.pdf
http://eprints.intimal.edu.my/128/
_version_ 1644541125947555840
score 13.211869

Similar Items