The usage of parallelization and distribution techniques in the field of ontology matching is of high interest for the semantic web community. This work presents an approach for managing the process of extending complex information structures as used in Urban Computing system by means of ontology matching considering parallelization and distribution techniques. Especially when thinking of matching large-scale ontologies coming from diverse resources, there is the need of considering an approach allowing for distributing the workload accordingly in order to address performance and scalability issues more efficiently. In this paper we present approaches for solving these issues by using parallelization and distribution techniques in order to improve scalability and performance of the ontology matching process.