Segmenting and Merging Domain-specific Ontology Modules for Clinical Informatics

Authors:
Chimezie Ogbuji;Sivaram Arabandi;Songmao Zhang;Guo-Qiang Zhang
Affiliations:
Cleveland Clinic, Cleveland OH, USA and Case Western Reserve University, Cleveland OH, USA;Case Western Reserve University, Cleveland OH, USA;Chinese Academy of Sciences, Beijing, P. R. China;Case Western Reserve University, Cleveland OH, USA
Venue:
Proceedings of the 2010 conference on Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS 2010)
Year:
2010

Citing 9
Cited 1

A methodology for ontology integration

Proceedings of the 1st international conference on Knowledge capture
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Modularisation of domain ontologies implemented in description logics and related formalisms including OWL

Proceedings of the 2nd international conference on Knowledge capture
A reference ontology for biomedical informatics: the foundational model of anatomy

Journal of Biomedical Informatics - Special issue: Unified medical language system
Web ontology segmentation: analysis, classification and use

Proceedings of the 15th international conference on World Wide Web
The foundational model of anatomy in OWL: Experience and perspectives

Web Semantics: Science, Services and Agents on the World Wide Web
Investigating subsumption in SNOMED CT: An exploration into large description logic-based biomedical terminologies

Artificial Intelligence in Medicine
Modular reuse of ontologies: theory and practice

Journal of Artificial Intelligence Research
CropCircles: topology sensitive visualization of OWL class hierarchies

ISWC'06 Proceedings of the 5th international conference on The Semantic Web

Integrating large, disparate biomedical ontologies to boost organ development network connectivity

DILS'12 Proceedings of the 8th international conference on Data Integration in the Life Sciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

A significant set of challenges to the use of large, source ontologies in the medical domain include: automated translation, customization of source ontologies, and performance issues associated with the use of logical reasoning systems to interpret the meaning of a domain captured in a formal knowledge representation. SNOMED-CT and FMA are two reference ontologies that cover much of the domain of clinical informatics and motivate a better means for re-use. In this paper, we present a method for segmenting and merging modules from these ontologies for a specific domain that preserve the meaning of the anatomy terms they have in common.