DataXFormer: Leveraging the Web for Semantic Transformations

Data transformation is a crucial step in data integration. While some transformations, such as liters to gallons, can be easily performed by applying a formula or a program on the input values, others, such as zip code to city, require sifting through a repository containing explicit value map-pings. There are already powerful systems that provide for-mulae and algorithms for transformations. However, the au-tomated identification of reference datasets to support value mapping remains largely unresolved. The Web is home to millions of tables with many containing explicit value map-pings. This is in addition to value mappings hidden be-hind Web forms. In this paper, we present DataXFormer, a transformation engine that leverages Web tables and Web forms to perform transformation tasks. In particular, we describe an inductive, filter-refine approach for identifying explicit transformations in a corpus of Web tables and an approach to dynamically retrieve and wrap Web forms. Ex-periments show that the combination of both resource types covers more than 80 % of transformation queries formulated by real-world users. 1