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A Session-Based System for Aligning Large Ontologies

Ontologies are a key technology for the Semantic Web. In different areas, a large number of ontologies have been developed so far by different people or organizations under the same domains and many of them contain overlapping information.

In order to get more benefit from different ontologies having inter-related knowledge they have to be aligned or merged. A number of systems have been developed for aligning and merging ontologies and various alignment strategies are used in these systems.

However, there is no system available which supports multiple alignment sessions for aligning large ontologies adequately. In this thesis work we propose a session-based framework for aligning and merging large ontologies. We have implemented two types of sessions, computation sessions to generate suggestions and validation sessions to validate these generated suggestions.

Furthermore after categorizing suggestions into accepted and rejected ones, we generated partial reference alignment (PRA) that can be used to compute similarities between terms and to filter mapping suggestions.

We have also proposed recommendation process integrated with computation and validation sessions in order to find out which matchers, and combinations are better to use for alignment process. Either computation and validation sessions may use the recommended settings or the user can select other matchers and combinations.
Source: Linköping University
Author: Kahn, Muzammil Zareen

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