Generalizing Matching Knowledge using Active Learning

Research on integrating small numbers of datasets suggests the use of customized matching rules in order to adapt to the patterns in the data and achieve better results. The state-of-the-art work on matching large numbers of datasets exploits attribute co-occurrence as well as the similarity of values between multiple sources. We build upon these research directions in order to develop a method for generalizing matching knowledge using minimal human intervention. The central idea of our research program is that even in large numbers of datasets of a specific domain patterns (matching knowledge) reoccur, and discovering those can facilitate the integration task. Our proposed approach plans to use and extend existing work of our group on schema and instance matching as well as on learning expressive rules with active learning. We plan to evaluate our approach on publicly available e-commerce data collected from the Web.