Nowadays, the need for well-structured ontologies in the medical domain is rising, especially due to the significant support these ontologies bring to a number of groundbreaking applications, such as intelligent medical diagnosis system and decision-support systems. Indeed, the considerable production of clinical data belonging to restricted subdomains has stressed the need for efficient methodologies to automatically process enormous amounts of un-structured, domain specific information in order to make use of the knowledge these data provide. In this work, we propose a lexicon-grammar based methodology for efficient information extraction and retrieval on unstructured medical records in order to enrich a simple ontology descriptive of such a kind of documents. We describe the NLP methodology for extracting RDF triples from unstructured medical records, and show how an existing ontology built by a domain expert can be populated with the set of triples and then enriched through its linking to external resources.
A Lexicon-Grammar Based Methodology for Ontology Population for e-Health Applications / Amato, Flora; De Santo, A.; Moscato, Vincenzo; Picariello, Antonio; Serpico, D.; Sperli', Giancarlo. - (2015), pp. 7185242.521-7185242.526. (Intervento presentato al convegno 9th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2015 tenutosi a Regional University of Blumenau (FURB), bra nel July 8-10, 2015) [10.1109/CISIS.2015.76].
A Lexicon-Grammar Based Methodology for Ontology Population for e-Health Applications
AMATO, FLORA;MOSCATO, VINCENZO;PICARIELLO, ANTONIO;SPERLI', GIANCARLO
2015
Abstract
Nowadays, the need for well-structured ontologies in the medical domain is rising, especially due to the significant support these ontologies bring to a number of groundbreaking applications, such as intelligent medical diagnosis system and decision-support systems. Indeed, the considerable production of clinical data belonging to restricted subdomains has stressed the need for efficient methodologies to automatically process enormous amounts of un-structured, domain specific information in order to make use of the knowledge these data provide. In this work, we propose a lexicon-grammar based methodology for efficient information extraction and retrieval on unstructured medical records in order to enrich a simple ontology descriptive of such a kind of documents. We describe the NLP methodology for extracting RDF triples from unstructured medical records, and show how an existing ontology built by a domain expert can be populated with the set of triples and then enriched through its linking to external resources.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.