By Panagiotis G. Ipeirotis
The World-Wide internet maintains to develop quickly, which makes exploiting all on hand info a problem. se's comparable to Google index an exceptional quantity of knowledge, yet nonetheless don't offer entry to worthwhile content material in textual content databases "hidden" at the back of seek interfaces. for instance, present se's principally forget about the contents of the Library of Congress, the united states Patent and Trademark database, newspaper documents, and lots of different priceless assets of data simply because their contents aren't "crawlable." even if, clients could be capable of finding the data that they wish with as little attempt as attainable, whether this data is crawlable or no longer. As an important step in the direction of this target, we have now designed algorithms that aid shopping and searching-the dominant methods of discovering details at the web-over "hidden-web" textual content databases.
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Extra info for Classifying and Searching Hidden-Web Text Databases
This technique ensures that the final number of matches for each category is not artificially inflated by documents that match multiple query probes. Unfortunately, if implemented in a naive way, this overlap-elimination strategy may result in rather long query 2. 2: Sending probes to the ACM Digital Library database with queries derived from a document classifier. probes, which might not be accepted by the databases. This problem could be partially solved by “breaking” the long queries into smaller conjunctive queries.
3: Generating rules from a set of weights wi and a threshold b. , it matches more correct documents than incorrect ones). 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].
Cn is an n × n matrix, where mij is the sum of the number of matches generated from documents in category Cj for category Ci query probes, divided by the total number of documents in category Cj . In a perfect setting, the probes for Ci match only documents in Ci and each document in Ci matches exactly one probe for Ci . In this case the confusion matrix is the identity matrix. The algorithm to create the normalized confusion matrix M is: 1. , the development set). 2. Create an auxiliary confusion matrix X = ( xij ) and set xij equal to the sum of the number of matches from Cj documents for category Ci query probes.
Classifying and Searching Hidden-Web Text Databases by Panagiotis G. Ipeirotis