Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Ontology Based Semantic Similarity Comparison of Documents
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Incorporating Ontology-Driven Similarity Knowledge into Functional Genomics: An Exploratory Study
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
A reference ontology for biomedical informatics: the foundational model of anatomy
Journal of Biomedical Informatics - Special issue: Unified medical language system
Representation in case-based reasoning
The Knowledge Engineering Review
Inter-patient distance metrics using SNOMED CT defining relationships
Journal of Biomedical Informatics
The foundational model of anatomy in OWL: Experience and perspectives
Web Semantics: Science, Services and Agents on the World Wide Web
Methodological Review: Data integration and genomic medicine
Journal of Biomedical Informatics
Incorporating biological domain knowledge into cluster validity assessment
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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Nowadays, ontologies and machine learning constitute two major technologies for domain-specific knowledge extraction which are actively used in knowledge-based systems of different kind including expert systems, decision support systems, knowledge discovery systems, etc. While the aim of these two technologies is the same - the extraction of useful knowledge - little is known about how the two sources of knowledge can be successfully integrated. Today the two technologies are used mainly separate; even though the knowledge extracted by the two is complementary and significant benefits can be obtained if the technologies were integrated. This problem is especially important for biomedicine where relevant data are often naturally complex having large dimensionality and including heterogeneous features, and where a large body of knowledge is available in the form of ontologies. In this paper we propose one approach for improving the performance of machine learning algorithms by integrating the knowledge provided by ontologies. The basic idea is to redefine the concept of similarity for complex heterogeneous data by incorporating available ontological knowledge, creating a bridge between the two technologies. Potential benefits and difficulties of this integration are discussed, two techniques for empirical evaluation and fine-tuning of feature ontologies are described, and an example from the field of paediatric cardiology is given.