By Topic

Using OODB modeling to partition a vocabulary into structurally and semantically uniform concept groups

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Li-min Liu ; Dept. of Math. & Comput. Sci., Kean Univ., Union, NJ, USA ; Halper, M. ; Geller, J. ; Perl, Y.

Controlled vocabularies (CVs) are networks of concepts that unify disparate terminologies and facilitate the process of information sharing within an application domain. We describe a general methodology for representing an existing CV as an object-oriented database (OODB), called an object-oriented vocabulary repository (OOVR). A formal description of the OOVR methodology, which is based on a structural abstraction technique, is given, along with an algorithmic description and a number of theorems pertaining to some of the methodology's formal characteristics. An OOVR offers a two-level (concept level and schema level) view of a CV, with the schema-level view serving as an important abstraction that can aid in orientation to the CV's contents. While an OOVR can also assist in traversals of the CV, we have identified certain special CV configurations where such traversals can be problematic. To address this, we introduce - based on the original methodology - an enhanced OOVR methodology that utilizes both structural and semantic features to partition and model a CV's constituent concepts. With its basis in the notions of area and the recursively defined articulation concept, an enhanced OOVR representation provides users with an improved CV view comprising groups of concepts that are uniform both in their structure and semantics. An algorithmic description of the singly-rooted OOVR methodology and theorems describing some of its formal properties are given. The results of applying it to a large existing CV are discussed

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:14 ,  Issue: 4 )