Using OODB Modeling to Partition a Vocabulary in Structurally and Semantically Uniform Concept Groups
Issue No.04 - July/August (2002 vol.14)
<p>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 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 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.</p>
Object-oriented databases, object-oriented models, object-oriented systems, knowledge representation, database models.
Li-min Liu, Michael Halper, James Geller, Yehoshua Perl, "Using OODB Modeling to Partition a Vocabulary in Structurally and Semantically Uniform Concept Groups", IEEE Transactions on Knowledge & Data Engineering, vol.14, no. 4, pp. 850-866, July/August 2002, doi:10.1109/TKDE.2002.1019218