Wrapper induction is the problem of automatically inferring a query from annotated web pages of the same template. This query should not only select the annotated content accurately but also other content following the same template. Beyond accurately matching the template, we consider two additional requirements: (1) wrappers should be robust against a large class of changes to the web pages, and (2) the induction process should be noise resistant, i.e., tolerate slightly erroneous (e.g., machine generated) samples. Key to our approach is a query language that is powerful enough to permit accurate selection, but limited enough to force noisy samples to be generalized into wrappers that select the likely intended items. We introduce such a language as subset of XPATH and show that even for such a restricted language, inducing optimal queries according to a suitable scoring is infeasible. Nevertheless, our wrapper induction framework infers highly robust and noise resistant queries. We evaluate the queries on snapshots from web pages that change over time as provided by the Internet Archive, and show that the induced queries are as robust as the human-made queries. The queries often survive hundreds sometimes thousands of days, with many changes to the relative position of the selected nodes (including changes on template level). This is due to the few and discriminative anchor (intermediately selected) nodes of the generated queries. The queries are highly resistant against positive noise (up to 50%) and negative noise (up to 20%).
Furche et al. REF studied the problems of both extraction robustness and noise resistant, but their method needs to annotate web pages manually and is not a completely automatic method.
Robust and Noise Resistant Wrapper Induction
SIGMOD '16
Computer Science