DSSim results for OAEI 2009

To solve the matching problem without rich literal information, a similarity propagation matcher with strong propagation condition (SSP matcher) is presented, and the

The presented MapPSO method regards the ontology alignment problem as an opti- misation problem which allows for the adaptation of a discrete variant of particle swarm optimisation

Then we give new features in instance matching compared with traditional ontology matching (schema matching) and introduce the specific techniques we used for the 3 different

Secondly, SOBOM utilizes Semantic Inductive Similarity Flooding algorithm to compute the similarity of the concepts between the sub-ontologies derived from the two

As only equivalence relationships will be evaluated in the alignment contest, we did not use this year the techniques which generate isA relationship (except in the Task 3) and

Our algorithm contains preprocess- ing, adaptation module for OAEI 2008, the basic block of algorihtm and the local align- ment process.. We created our own persistent model

In the context of ontology matching, the class of compound nouns semantic relation detection algorithms may be used in order to determine such relations within ontology

To solve the matching problem without rich literal information, a similarity propagation matcher with strong propagation condition (SSP matcher) is presented, and the