Type-rule-based Wrapper Generation

Biological data sources are useful to bioinformatics researches. Several computational tools have been developed so that these data sources can be used as easily as possible. Most of biological data has been provided over the web. Web data is almost represented in unstructured format and cannot be queried using traditional querying language. Furthermore, the problems, which 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 human being 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.