Classifying and Searching Hidden-Web Text Databases by Panagiotis G. Ipeirotis

By Panagiotis G. Ipeirotis

The World-Wide internet keeps to develop quickly, which makes exploiting all to be had info a problem. se's equivalent to Google index an unparalleled quantity of data, yet nonetheless don't offer entry to helpful content material in textual content databases "hidden" in the back of seek interfaces. for instance, present se's principally forget about the contents of the Library of Congress, the USA Patent and Trademark database, newspaper information, and plenty of different worthy assets of knowledge simply because their contents are usually not "crawlable." although, clients will be capable of finding the data that they want with as little attempt as attainable, whether this knowledge is crawlable or now not. As an important step in the direction of this aim, we now have designed algorithms that aid shopping and searching-the dominant methods of discovering info at the web-over "hidden-web" textual content databases.

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3 Evaluation Metrics We evaluate classification algorithms by comparing the approximate classification Approximate(D) that they produce against the ideal classification Ideal(D). , that also appear in Ideal(D)). However, this would not capture the nuances of hierarchical classification. ” The metric above would consider this classification as absolutely wrong, 2. ” With this in mind, we adapt the precision and recall metrics from information retrieval [CM63]. We first introduce an auxiliary definition.

33. An important property of classification strategies over the web is scalability. We measure the efficiency of the various techniques that we compare by modeling their cost. More specifically, the main cost we quantify is the number of “interactions” required with the database to be classified, where each interaction is either a query submission (needed for all three techniques) or the retrieval of a database document (needed only for Document Sampling and Title-based Querying). 4 Experimental Results believe that they would not affect our conclusions, since these costs are CPUbased and are small compared to the cost of interacting with the databases over the Internet.

The terms that form an extracted rule are removed from further consideration and will not participate in later iterations of the algorithm. Also, training examples that match a produced rule are removed from the training set, and will not be used in later iterations. To proceed to the next iteration, the algorithm expands unused term sets by one term, in a spirit similar to an algorithm for finding “association rules” [AS94]. , the sets of terms whose sum of weights is smaller than b) to get new itemsets with larger support.

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