Instance-based ontology matching by instance matching
|has title::Instance-based ontology matching by instance enrichment|
|Master:||project within::Technical Artificial Intelligence|
|Student name:||student name::Balthasar Schopman|
|Second reader:||has second reader::Antoine Isaac, Shenghui Wang|
Abstract KIM 1
The Ontology Matching (OM) problem is an important barrier to break in order to use Semantic Web standards on the world wide web. Several kinds of OM techniques exist. Instance-based OM (IbOM) is a promising OM technique, which is gaining popularity amongst researchers. IbOM uses the extensional information of concepts to determine whether or not a pair of concepts is related. The extensional information of a concept consists of the instances with which that concept is annotated.
While IbOM has many strengths, a weakness is that in order to match two ontologies a data-set that is annotated with both ontologies is required. In practice data-sets are often annotated with a single ontology, rendering IbOM rarely applicable. However, in my KIM presentation, I will suggest a method that enables IbOM. This is done by using a similarity measure to calculate the distance between instances of two data-sets. To every instance in the data-sets the annotations of the most similar instance in the other data-set are added, creating a dually annotated data-set and enabling the possiblity to apply IbOM. This technique has proved to be successful, rendering it promising for IbOM research.