Concept Similarity Under the Agent’s Preferences for the Description Logic \(\mathcal {A}\!\mathcal {L}\!\mathcal {E}\!\mathcal {H}\)

Computing the degree of concept similarity is an essential problem in description logic ontologies as it has contributions in various applications. However, many computational approaches to concept similarity do not take into account the logical relationships defined in an ontology. Moreover, they cannot be personalized to subjective factors (i.e. the agent’s preferences). This work introduces a computational approach to concept similarity for the description logic \(\mathcal {A}\!\mathcal {L}\!\mathcal {E}\!\mathcal {H}\). Our approach computes the degree of similarity between two concept descriptions structurally under the agent’s preferences. Hence, the derived degree is analyzed based on the logical definitions defined in an ontology. We also illustrate its applicability in rice disease detection, in which a farmer queries for relevant disease based on an agricultural observation.