Seminar Details
| Date |
21-3-2006 |
| Time |
12:00 |
| Room/Location |
Sala conferenze, DISI, num. 322 - 3° piano |
| Title |
Abstraction Networks for Controlled Terminologies |
| Speaker |
Dr. Michael Halper , Professor, Department of Mathematics & Computer Science |
| Affiliation |
Department of Mathematics & Computer Science , Kean University Union, NJ 07083-0411 USA |
| Link |
http://www.kean.edu/~mhalper
|
| Abstract |
A controlled terminology is a structure that houses knowledge from some
domain, such as biomedicine, in the form of concepts, subsumption
(IS-A) links, and semantic relationships. Controlled terminologies
have been variously referred to as vocabularies, ontologies, or
terminological knowledge bases. Among the primary benefits of such
systems are their support for information sharing and integration,
decision-support, and ad hoc querying of domain knowledge. Controlled
terminologies have found widespread acceptance and usage within the
biomedical community. Examples include the National Cancer Institute
Thesaurus (NCIT), developed as part of NCI's Enterprise Vocabulary
Services project, and the Systematized Nomenclature of Medicine,
Clinical Terms (SNOMED CT), developed through a joint effort of the
College of American Pathologists and the UK's National Health Service.
While controlled terminologies have proven to be invaluable resources,
they do, however, tend to be very large and complex. For example, NCIT
currently comprises over 42,000 concepts, while SNOMED CT has more than
360,000 concepts. This poses serious problems for users and
maintenance personnel alike. In particular, quality assurance can be
difficult.
In this talk, I present automated techniques for partitioning a
controlled terminology into smaller groups of concepts based on
relationship patterns and subsumption groupings. Two abstraction
networks, called the area taxonomy and the p-area taxonomy, are derived
from the partitions. The high-level views afforded by these
abstraction networks form the basis for systematic auditing. For
example, the taxonomies tend to highlight concept errors that manifest
themselves as irregularities at the abstract level. Among the kinds of
errors are conceptual ambiguity, omission of concepts, and concept
misclassification. The partitioning and auditing methodologies are
demonstrated on one of NCIT's top-level hierarchies. Errors discovered
during the auditing process are presented.
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