11. A system for automatically identifying semi-structured data from a semi-structured web site by executing instructions stored in a computer-readable memory by a computer processor, the system comprising: means for analyzing links and pages on the semi-structured web site using a set of heterogeneous experts, each of the experts focusing on a respective type of structure included in the semi-structured web site; means for identifying, by the set of experts, similarities and dissimilarities between the analyzed links and pages; means for clustering pages and text segments based on the similarities and dissimilarities identified by at least two experts in the set of heterogeneous experts, wherein each of the at least two experts produces hints indicating whether two items should be together in a cluster, the hints containing respective levels of confidence; and wherein the clustering of text segments comprises: finding page clusters; determining a set of text segments for each of the found pare clusters; and clustering text segments of the set of text segments; means for identifying, based on the clustering of pages and text segments, at least some of the semi-structured data to be extracted from the semi-structured web site; means for extracting the at least some of the identified semi-structured data; and means for transforming the extracted semi-structured data into a relational structured form.
11. A system for automatically identifying semi-structured data from a semi-structured web site by executing instructions stored in a computer-readable memory by a computer processor, the system comprising: means for analyzing links and pages on the semi-structured web site using a set of heterogeneous experts, each of the experts focusing on a respective type of structure included in the semi-structured web site; means for identifying, by the set of experts, similarities and dissimilarities between the analyzed links and pages; means for clustering pages and text segments based on the similarities and dissimilarities identified by at least two experts in the set of heterogeneous experts, wherein each of the at least two experts produces hints indicating whether two items should be together in a cluster, the hints containing respective levels of confidence; and wherein the clustering of text segments comprises: finding page clusters; determining a set of text segments for each of the found pare clusters; and clustering text segments of the set of text segments; means for identifying, based on the clustering of pages and text segments, at least some of the semi-structured data to be extracted from the semi-structured web site; means for extracting the at least some of the identified semi-structured data; and means for transforming the extracted semi-structured data into a relational structured form. 18. The system of claim 11 , further comprising means for adding to the hints a binary hint that indicates that a particular pair of items are in the same cluster.