The amount of biomedical information that is disseminated over the Web increases every day. This rich resource is used to find solutions to challenges across the life sciences. The Semantic Web for life sciences shows promise for effectively and efficiently locating, integrating, querying and inferring related information that is needed in daily biomedical research. One of the key technologies in the Semantic Web is ontologies, which furnish the semantics of the Semantic Web. A large number of biomedical ontologies have been developed. Many of these ontologies contain overlapping information, but it is unlikely that eventually there will be one single set of standard ontologies to which everyone will conform. Therefore, applications often need to deal with multiple overlapping ontologies, but the heterogeneity of ontologies hampers interoperability between different ontologies. Aligning ontologies, i.e. identifying relationships between different ontologies, aims to overcome this problem. A number of ontology alignment systems have been developed. In these systems various techniques and ideas have been proposed to facilitate identification of alignments between ontologies. However, there still is a range of issues to be addressed when we have alignment problems at hand. The work in this thesis contributes to three different aspects of identification of high quality alignments: 1) Ontology alignment strategies and systems. We surveyed the existing ontology alignment systems, and proposed a general ontology alignment framework. Most existing systems can be seen as instantiations of the framework. Also, we developed a system for aligning biomedical ontologies (SAMBO) according to this framework. We implemented various alignment strategies in the system. 2) Evaluation of ontology alignment strategies. We developed and implemented the KitAMO framework for comparative evaluation of different alignment strategies, and we evaluated different alignment strategies using the implementation. 3) Recommending optimal alignment strategies for different applications. We proposed a method for making recommendations.