MapPSO results for OAEI 2008

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

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

We can observe from Table 9, that all the systems that participated in the directory track in 2007 and 2008 (ASMOV, DSSim, Lily and RiMOM), have increased their precision

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

The second stage of AROMA discovers the subsumption relations by using the association rule model and the implication intensity measure [Gras et al., 2008]. an implication allowing

The Automated Semantic Mapping of Ontologies with Validation (ASMOV) algorithm for ontology alignment was one of the top performing algorithms in the 2007

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