Abstract—Over the last years, link discovery frameworks have been employed successfully to create links between knowledge bases. Consequently, repositories of high-quality link specifica-tions have been created and made available on the Web. The basic question underlying this work is the following: Can the specifications in these repositories be reused to ease the detection of link specifications between unlinked knowledge bases? In this paper, we address this question by presenting a formal transfer learning framework that allows detecting existing specifications that can be used as templates for specifying links between previ-ously unlinked knowledge bases. We discuss both the advantages and the limitations of such an approach for determining link specifications. We evaluate our approach on a variety of link specifications from several domains and show that the detection of accurate link specifications for use as templates can be achieved with high reliability. I.