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.

The session-based SAMBO is an ontology alignment system involving MySQL to store matching results. Currently, SAMBO is able to align most ontologies within acceptable time. However, when it comes to large scale ontologies, SAMBO fails to reach the target. Thus, the main purpose of this thesis work is to improve the performance of SAMBO, especially in the case of matching large scale ontologies.  To reach the purpose, a comprehensive literature study and an investigation on two outstanding large scale ontology system are carried out with the aim of setting the improvement directions. A detailed investigation on the existing SAMBO is conducted to figure out in which aspects the system can be improved. Parallel matching process optimization and data management optimization are determined as the primary optimization goal of the thesis work. In the following, a few relevant techniques are studied and compared. Finally, an optimized design is proposed and implemented.  System testing results of the improved SAMBO show that both parallel matching process optimization and data management optimization contribute greatly to improve the performance of SAMBO. However the execution time of SAMBO to align large scale ontologies with database interaction is still unacceptable.

With the emerging amount of information available in the internet, how to make full use of this information becomes an urgent issue. One of the solutions is using ontology alignment to aggregate different sources of information in order to get comprehensive and complete information. Scalability is a problem regarding the ontology alignment and it can be settled down by reducing the search space of mapping suggestions. In this paper we propose an automated procedure mainly using different clustering techniques to prune the search space. The main focus of this paper is to evaluate different clustering related techniques to be applied in our system. K-means, Chameleon and Birch have been studied and evaluated, every parameter in these clustering algorithms is studied by doing experiments separately, in order to find the best clustering setting to the ontology clustering problem. Four different similarity assignment methods are researched and analyzed as well. Tfidf vectors and cosine similarity are used to identify the similar clusters in the two ontologies, experiments about threshold of cosine similarity are made to get the most suitable value. Our system successfully builds an automated procedure to generate reduced search space for ontology alignment, on one hand, the result shows that it reduces twenty to ninety times of comparisons that the ontology alignment was supposed to make, the precision goes up as well. On the other hand, it only needs one to two minutes of execution time, meanwhile the recall and f-score only drop down a little bit. The trade- off is acceptable for the ontology alignment system which will take tens of minutes to generate the ontology alignment of the same ontology set. As a result, the large scale ontology alignment becomes more computable and feasible.