LiSTOMS: A Light-Weighted Self-Tuning Ontology Mapping System

Abstract

We argue that it is more practical to address the ontology mapping self-tuning problem in a whole system context instead of in a single matcher context. In this paper we introduce RMOMS, a Reference Model for Ontology Mapping Systems, consisting of six parts, the Preprocessor, the Dispatcher, the Matcher(s), the Aggregator, the Pruner, and the User Interface, with which to disassemble the self-tuning problem into more feasible units. We propose Maximum Weight Bipartite Graph Matching method for self-tuning matchers and Stable Match method for self-tuning aggregator, and test them in LiSTOMS, a light-weighted prototype sample of RMOMS. With comparison with some notable systems, LiSTOMS shows leading recall rate and competing precision rate.