A learning classifier-based approach to aligning data items and labels
BNCOD'13 Proceedings of the 29th British National conference on Big Data
Scalable and noise tolerant web knowledge extraction for search task simplification
Decision Support Systems
| Hi-index | 0.00 |
We present in this paper a novel approach for extracting structured data from the Web, whose goal is to harvest real-world items from template-based HTML pages (the structured Web). It illustrates a two-phase querying of the Web, in which an intentional description of the data that is targeted is first provided, in a flexible and widely applicable manner. The extraction process leverages then both the input description and the source structure. Our approach is domain-independent, in the sense that it applies to any relation, either flat or nested, describing real-world items. Extensive experiments on five different domains and comparison with the main state of the art extraction systems from literature illustrate its flexibility and precision. We advocate via our technique that automatic extraction and integration of complex structured data can be done fast and effectively, when the redundancy of the Web meets knowledge over the to-be-extracted data.