(Semi-)automatic mapping — also called (semi-)automatic alignment — of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. In Peer-to-Peer systems, however, we frequently encounter the situation where large ontological structures must be mapped onto each other in a few seconds or less in order to achieve practical feasibility of semantic translation between peers.
We here present QOM (acronym for Quick Ontology Mapping), an approach that follows Herb Simon’s model of men, where he argues that human decision making is not aiming at optimality, but at satisfying the decision maker by achieving a sufficient degree of quality. We show that QOM has lower run-time complexity than existing approaches. Then, we show in experiments that this theoretical investigation translates into practical benefits. While QOM gives up some of the possibilities for producing high-quality results in favor of efficiency, our experiments show that this loss of quality is marginal, hence satisficing (=satisfying + sufficient).