Autonomic Wrapper Induction using Minimal Type System from Web Data

Biological and genomic source integration has become a major research field. Most of biological data has been provided over the Web. This Web data is unstructured and cannot be queried using traditional querying language. Furthermore, the problems that integration of biological data faces come from several factors such as the various data types, presentations and formats. So, it is not easy to find the desired data from diverse data sources. Although humans can easily understand Web data, which are heterogeneous and unstructured, it is impossible for machine itself to figure it out. In order for machine to extract data from the Web, it requires knowledge of both their structures and contents. We propose a novel architecture for automatic wrapper induction that exploits a user supplied type system and an ontology for establishing schema correspondence precisely and efficiently. In this paper, the type system helps recognize target data and improves precision of schema matching which is impossible without manual intervention