The Design and Implementation of Optimization Approaches for Large Scale Ontology Alignment in SAMBO

Abstract: The current World Wide Web provides a convenient way for people to acquire information,but it does not have the ability to manipulate semantics. In other words, peoplecan access data from web pages efficiently but computer programs cannot satisfy effectivedata reuse and sharing. Tim Berners-Lee as the inventor ofWorldWideWeb together withJames Hendler and Ora Lassila, proposed the idea of Semantic Web that is expected as anevolution to existing Web. The knowledge representation for Semantic Web witnessed thedevelopment from extensible makeup language (XML) and resource description framework(RDF) to ontologies. A large quantity of researchers utilize ontologies to expressconcepts, relations and relevant semantics in specific domains. However, different researchersmay have diverse comprehension about knowledge that brings inconsistentinformation in same or similar ontologies. SAMBO is an ontology alignment system that was designed and implemented by ADITof Linköping University in 2005. Shortly after implementation, SAMBO could accomplishmost tasks of ontology alignment. Nevertheless, as the scale grows rapidly, SAMBO couldnot achieve large scale ontology alignment. The primary job of this thesis is to optimizeexisting SAMBO system to fulfill alignment of large scale ontologies. The principal parts of this thesis are as follows. First, we achieve an analysis on currenttop ontology alignment systems, AML and LogMap which are capable of aligning largescale ontologies. This analysis aims to obtain the features in the design of high-quality systems.Then, we analyze existing SAMBO to figure out which aspects need to be optimized.We obtain the result that SAMBO should be improved in data structure, database designand parallel matching. Thus, we propose the design of optimization approaches and givethe implementation. Finally, we evaluate the new system with large scale ontologies andacquire desired results.