Instance-based ontology matching by instance matching

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has title::Instance-based ontology matching by instance enrichment
status: ongoing
Master: project within::Technical Artificial Intelligence
Student name: student name::Balthasar Schopman
number: student number::1431838
Dates
Start start date:=2009/01/01
End end date:=2009/07/01
Supervision
Supervisor: Stefan Schlobach
Second reader: has second reader::Antoine Isaac, Shenghui Wang
Poster: has poster::Media:Media:Posternaam.pdf

Signature supervisor



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Abstract

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.