Large amount of data coming from different sources and formats is available on the web
which generates heterogeneity problem. Semantic web technologies play an important role for collecting,
merging, matching and aggregating big data from heterogeneous resources by determining
the semantic correspondence between the entities. However, achieving good efficiency is major
challenge for large scale ontology matching task.
Objective: We propose a PBOM framework for coping with the large scale ontology matching
Methods: Our proposal first selects the source ontology and calculates the similarity of concepts
within source ontology by using Lin measure. We use clustering algorithm for partition of the
source ontology, obtained clusters of source ontology then used to divide the target ontology. During
matching process, we run matchers from the pool of the matchers over each clusters. We aggregate
the result of element level matchers and structure level matchers according to weighted
sum aggregation. Each cluster is executed in its processor in parallel with other clusters.
Result: We have presented step wise execution of proposed approach over one cluster of source
and target ontology. The evaluation of our framework is performed by OAEI datasets of bibliographic
benchmark 2014, biomedical track 2015 and anatomy 2016.
Conclusion: Results show that, the performance of our approach is better in term of F-measure.
The combination of clustering algorithm and parallel processing reduces the memory space and
time complexity of matching process.