Structural Subsumption Based Similarity Measures: applications with a medical ontology and in image object recognition
From International Center for Computational Logic
Structural Subsumption Based Similarity Measures: applications with a medical ontology and in image object recognition
Talk by Boontawee Suntisrivaraporn
- Location: APB 1004
- Start: 10. June 2016 at 1:30 pm
- End: 10. June 2016 at 2:30 pm
- Research group: Automata Theory
- iCal
Description Logics (DLs) are a family of logic-based knowledge representation formalisms, which can be used to develop ontologies in a formally well-founded way. The standard reasoning service of subsumption has proved indispensable in ontology design and maintenance. This checks, relative to the logical definitions in the ontology, whether one concept is more general/specific than another. When no subsumption relationship is identified, however, no information about the two concepts can be given. We introduce a similarity measure based on the structural subsumption algorithm for the DL EL and then generalize this to one for the DL ALEH. The proposed similarity measures compute a numerical degree of similarity between two concept descriptions despite not being in the subsumption relation. In this talk, two application scenarios will be discussed: the first is about new findings in SNOMED CT; whereas the second is the employment of the similarity measure in an image object recognition framework.
