Location of Repository

Reasoning by analogy in the generation of domain acceptable ontology refinements

By L. Moss, D. Sleeman and Malcolm Sim


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

Topics: QA75
Publisher: Springer
Year: 2010
OAI identifier: oai:eprints.gla.ac.uk:48782
Provided by: Enlighten

Suggested articles



  1. (2006). A Fine-Grained Approach to Resolving Unsatis Ontologies. doi
  2. (2006). A Framework for Ontology Evolution in Collaborative Environments. doi
  3. (2009). Accessed
  4. (2004). An overview of OntoClean. doi
  5. (2002). Analogical Reasoning, Psychology of. doi
  6. (1990). Automating the Re of Knowledge-Based Systems.
  7. (2008). Laconic and Precise Justi in OWL. doi
  8. (2003). Learner: A System for Acquiring Commonsense Knowledge by Analogy. doi
  9. (2007). Ontology Learning: State of the Art and Open Issues. doi
  10. (2008). Ontology-Based Relevance Assessment: An Evaluation of Dierent Semantic Similarity Measures. doi
  11. (2008). Ontology-Based Relevance Assessment: An Evaluation of Different Semantic Similarity Measures. doi
  12. (2010). Ontology-Driven Hypothesis Generation to Explain Anomalous Patient Responses to Treatment. Knowledge-Based Systems, doi
  13. (2008). Using Background Knowledge for Ontology Evolution. doi
  14. (2004). Why Evaluate Ontology Technologies? Because They Work!
  15. (2008). Why Ontology Evolution Is Essential in Modelling Scienti Discovery.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.