Using explicit ontologies in KBS development
International Journal of Human-Computer Studies
Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Journal of Biomedical Informatics - Special issue: Unified medical language system
Ontologies: How can They be Built?
Knowledge and Information Systems
MedPost: a part-of-speech tagger for bioMedical text
Bioinformatics
Journal of the American Society for Information Science and Technology
Conceptualizing the world: lessons from history
Journal of Biomedical Informatics - Special issue: Biomedical ontologies
Journal of Biomedical Informatics
Distributional measures of concept-distance: a task-oriented evaluation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A four stage approach for ontology-based health information system design
Artificial Intelligence in Medicine
Journal of the American Society for Information Science and Technology
Discovering discovery patterns with predication-based Semantic Indexing
Journal of Biomedical Informatics
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We describe a domain-independent methodology to extend SemRep coverage beyond the biomedical domain. SemRep, a natural language processing application originally designed for biomedical texts, uses the knowledge sources provided by the Unified Medical Language System (UMLS^(C)). Ontological and terminological extensions to the system are needed in order to support other areas of knowledge. We extended SemRep's application by developing a semantic representation of a previously unsupported domain. This was achieved by adapting well-known ontology engineering phases and integrating them with the UMLS knowledge sources on which SemRep crucially depends. While the process to extend SemRep coverage has been successfully applied in earlier projects, this paper presents in detail the step-wise approach we followed and the mechanisms implemented. A case study in the field of medical informatics illustrates how the ontology engineering phases have been adapted for optimal integration with the UMLS. We provide qualitative and quantitative results, which indicate the validity and usefulness of our methodology.