Abstract
The access to medical information (journals, blogs, web-pages, dictionaries, and texts) has been increased due to availability of many digital media. In particular, finding an appropriate structure that represents the information contained in texts is not a trivial task. One of the structures for modeling the knowledge are ontologies. An ontology refers to a conceptualization of a specific domain of knowledge. Ontologies are especially useful because they support the exchange and sharing of information as well as reasoning tasks. The usage of ontologies in medicine is mainly focussed in the representation and organization of medical terminologies. Ontology learning techniques have emerged as a set of techniques to get ontologies from unstructured information. This paper describes a new ontology learning approach that consists of a method for the acquisition of concepts and its corresponding taxonomic relations, where also axioms disjointWith and equivalentClass are learned from text without human intervention. The source of knowledge involves files about medical domain. Our approach is divided into two stages, the first part corresponds to discover hierarchical relations and the second part to the axiom extraction. Our automatic ontology learning approach shows better results compared against previous work, giving rise to more expressive ontologies.
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Notes
A hyponym can be defined as a word of more specific meaning than a general or superordinate term applicable to it. By contrast, a hypernym is a word with a broad meaning constituting a category under which more specific words fall. For example, Apple, Banana, and Pear are hyponyms of Fruit whereas Fruit is a hypernym of Apple, Banana, and Pear.
according to Web Ontology Language (OWL). http://www.w3.org/TR/owl-features/
Ontology for human disease http://www.berkeleybop.org/ontologies/doid.owl
NE is a named entity and NP is a noun phrase.
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Rios-Alvarado, A.B., Lopez-Arevalo, I., Tello-Leal, E. et al. An Approach for Learning Expressive Ontologies in Medical Domain. J Med Syst 39, 75 (2015). https://doi.org/10.1007/s10916-015-0261-z
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DOI: https://doi.org/10.1007/s10916-015-0261-z