A Framework for Generating Attribute Extractors for Web Data Sources

Abstract

To cope with the irregularities of typical semistructured Web data, extraction tools usually break the extraction task in two phases: an extraction phase, in which atomic attribute values are extracted from Web pages, and an assembling phase, in which these atomic values are grouped to form complex objects. As a consequence, the whole process is highly dependent on the attribute values collected in the first phase. All attribute values of interest should be properly recognized and spurious values should be discarded. Thus, attribute values extraction is an important problem. In this paper, we propose a new framework for generating attribute value extractors. The main appeal of this framework is that it can be adapted for dealing with specific types of data sources and to incorporate distinct types of heuristics for achieving good extraction performance. To demonstrate the feasibility of this proposal, we present an implementation of this framework for data-rich Web pages and show how a number of simple heuristics, some of them presented in the recent literature, can be incorporated into this framework. We also show experimental results and, in most cases, our results are at least as good as results previously presented in the literature.