Discovering Linkage Points over Web Data

A basic step in integration is the identification of linkage points, i.e., finding attributes that are shared (or related) between data sources, and that can be used to match records or entities across sources. This is usually performed using a match operator, that associates attributes of one database to another. However, the massive growth in the amount and variety of unstructured and semistructured data on the Web has created new challenges for this task. Such data sources often do not have a fixed pre-defined schema and contain large numbers of diverse attributes. Furthermore, the end goal is not schema alignment as these schemas may be too heterogeneous (and dynamic) to meaningfully align. Rather, the goal is to align any overlapping data shared by these sources. We will show that even attributes with different meanings (that would not qualify as schema matches) can sometimes be useful in aligning data. The solution we propose in this paper replaces the basic schemamatching step with a more complex instance-based schema analysis and linkage discovery. We present a framework consisting of a library of efficient lexical analyzers and similarity functions, and a set of search algorithms for effective and efficient identification of linkage points over Web data. We experimentally evaluate the effectiveness of our proposed algorithms in real-world integration scenarios in several domains.