A Hybrid Approach to Learn Description Logic Ontology from Texts
نویسندگان
چکیده
Augmenting formal medical knowledge is neither manually nor automatically straightforward. However, this process can benefit from rich information in narrative texts, such as scientific publications. Snomed-supervised relation extraction has been proposed as an approach for mining knowledge from texts in an unsupervised way. It can catch not only superclass/subclass relations but also existential restrictions; hence produce more precise concept definitions. Based on this approach, the present work aims to develop a system that takes biomedical texts as input and outputs the corresponding EL++ concept definitions. Several extra features are introduced in the system, such as generating general class inclusions (GCIs) and negative concept names. Moreover, the system allows users to trace textual causes for a generated definition, and also give feedback (i.e. correction of the definition) to the system to retrain its inner model, a mechanism for ameliorating the system via interaction with domain experts.
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