Ontology Matching is among the most used techniques for hetrogeniety resolution; however, effective ontology matching is a computationally intensive operation requiring optimized matching algorithms to be executed over candidate ontologies. SPHeRe is a performance-based initiative that improves ontology matching performance by exploiting parallelism over multicore Desktops and Cloud Platforms.


  • Implement parallelism wherever needed from ontology loading till bridge ontology dilivery.
  • A thread-safe ontology model for multithreaded environment.
  • Mechanism to reduce memory footprint during matching.
  • Better computational resource utilization.
  • Combination of ontology matching techniques to generate accurate mappings among heterogeneous ontologies.



  • Improved overall performance over multicore platforms.
  • Thread-safe and Lightweight ontology model, ideal for cloud platforms with limited resources.
  • Better scalability.
  • Library of ontology matching techniques.
  • Mapping patterns for bridge ontology creation.


  • Better performance during ontology matching by end-to-end parallelism (10 times performance speed up over 4 node cloud platform).
  • Thread-safe and Lightweight ontology model, producing lower memory strains with smaller memory footprint during executions (8 times smaller memory footprint).
  • Better scalability by thread-level parallelism over available computational resources.
  • Classification of matching algorithms by their ontology needs with combinational execution.
  • Flexible ontology mapping representation scheme for quality management of mapping repository.