Reasoning by Analogy in the Generation of Domain Acceptable Ontology Renements

Laura Elizabeth Moss, Derek Sleeman, Malcolm Sim

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Refinements generated for a knowledge base often involve
the learning of new knowledge to be added to or replace existing parts
of a knowledge base. However, the justifiability of the refinement in the
context of the domain (domain acceptability) is often overlooked. The
work reported in this paper describes an approach to the generation
of domain acceptable refinements for incomplete and incorrect ontology
individuals through reasoning by analogy using existing domain knowledge.
To illustrate this approach, individuals for refinement are identified
during the application of a knowledge-based system, EIRA; when EIRA
fails in its task, areas of its domain ontology are identified as requiring
refinement. Refinements are subsequently generated by identifying and
reasoning with similar individuals from the domain ontology. To evaluate
this approach EIRA has been applied to the Intensive Care Unit (ICU)
domain. An evaluation (by a domain expert) of the refinements generated
by EIRA has indicated that this approach successfully produces
domain acceptable refinements.
Original languageEnglish
Title of host publicationEKAW 2010 Proceedings 17th International Conference
Subtitle of host publicationKnowledge Engineering and Management by the Masses
Place of PublicationBerlin
PublisherSpringer
Pages534-543
Number of pages10
ISBN (Print)978-3-642-16437-8, 3642164374
Publication statusPublished - Oct 2010

Keywords

  • Ontology Refinement
  • Analogical Reasoning
  • Medicine

Fingerprint Dive into the research topics of 'Reasoning by Analogy in the Generation of Domain Acceptable Ontology Renements'. Together they form a unique fingerprint.

  • Cite this

    Moss, L. E., Sleeman, D., & Sim, M. (2010). Reasoning by Analogy in the Generation of Domain Acceptable Ontology Renements. In EKAW 2010 Proceedings 17th International Conference: Knowledge Engineering and Management by the Masses (pp. 534-543). Springer .