Learning Formal Definitions for Snomed CT from Text

Abstract. Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. In this paper we present an approach for the extraction of Snomed CT definitions from natural language text. We test and evaluate the approach using two types of texts.

ONTOCOM revisited: towards accurate cost predictions for ontology development projects

Reliable methods to assess the costs and benefits of ontologies are an important instrument to demonstrate the tangible business value of semantic technologies within enterprises, as an argument to encourage their wide-scale adoption. The economic aspects of ontologies have been investigated in previous work of ours. With ONTOCOM we proposed a cost estimation model for ontologies and ontology development projects. This paper revisits this model and presents its latest achievements. We report on a comprehensive calibration of ONTOCOM based on a considerably larger data set of 148 ontology development projects. The calibration used a combination of statistical methods, ranging from preliminary data analysis to regression and Bayes analysis, and resulted a significant improvement of the prediction quality of up to 50%. In addition, the availability of a representative data set allowed us to identify meaningful directions for customizing the generic cost model along particular types of ontologies, and ontology-like structures as those specific to the emerging Web 3.0. Last but not least, we developed a software tool that allows ontology development project managers to easily use and adapt and to systematically calibrate the model, thus facilitating its adoption in real-world projects