Evaluating Ontologies with NLP-Based Terminologies --A Case Study on ACGT and Its Master Ontology

Authors:
Gintarė Grigonytė;Mathias Brochhausen;Luis Martín;Manolis Tsiknakis;Johann Haller
Affiliations:
Department for Applied Linguistics, Translation and Interpreting, Saarland University, Germany;Institute for Formal Ontology and Medical Information Science, Saarland University, Germany;Biomedical Informatics Group, Artificial Intelligence Laboratory, School of Computer Science, Universidad Politécnica de Madrid, Spain;Foundation for Research and Technology Hellas (FORTH), Institute of Computer Science, Greece;Institute of the Society for the Promotion of Applied Information Sciences at the Saarland University, Germany
Venue:
Proceedings of the 2010 conference on Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS 2010)
Year:
2010

Citing 4
Cited 0

A reference ontology for biomedical informatics: the foundational model of anatomy

Journal of Biomedical Informatics - Special issue: Unified medical language system
Data access and management in ACGT: tools to solve syntactic and semantic heterogeneities between clinical and image databases

ER'07 Proceedings of the 2007 conference on Advances in conceptual modeling: foundations and applications
Modelling ontology evaluation and validation

ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
A Semantic Grid Infrastructure Enabling Integrated Access and Analysis of Multilevel Biomedical Data in Support of Postgenomic Clinical Trials on Cancer

IEEE Transactions on Information Technology in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Natural language processing (NLP) plays a major role in knowledge engineering. However, NLP's usage is traditionally being seen as a means of extracting knowledge required for building knowledge resources, i.e. ontologies, knowledge bases. When it comes to the evaluation of these knowledge artefacts, then general trends are: expert reviewing, evaluating against existing ontologies and democratic ranking. We propose a new approach for evaluating domain coverage of application ontologies which is based on NLP techniques. The latter can be seen as one way of bridging the gap between terminologies and ontologies in order to create user-understandable expert systems.