A hybrid approach to learn description logic ontology from texts

Y. Ma, A. Syamsiyah

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

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
Original languageEnglish
Title of host publicationProceedings of the ISWC 2014 Posters Demonstrations Track a track within the 13th International Semantic Web Conference, ISWC 2014, Riva del Garda, Italy, October 21, 2014.
Pages421-424
Number of pages4
Publication statusPublished - 2014
Externally publishedYes

Publication series

NameCeur Workshop Proceedings
Volume1272

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