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Silberschatz Korth Sudarshan: Database System Concepts, Fourth Edition
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22 Advanced Querying and Information Retrieval
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The McGraw Hill Companies, 2001
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The intersection, S1 S2 Sn , of the sets gives the document identi ers of the desired set of documents The or operation gives the set of all documents that contain at least one of the keywords K1 , K2 , , Kn We implement the or operation by computing the union, S1 S2 Sn , of the sets The not operation nds documents that do not contain a speci ed keyword Ki Given a set of document identi ers S, we can eliminate documents that contain the speci ed keyword Ki by taking the difference S Si , where Si is the set of identi ers of documents that contain the keyword Ki Given a set of keywords in a query, many information retrieval systems do not insist that the retrieved documents contain all the keywords (unless an and operation is explicitly used) In this case, all documents containing at least one of the words are retrieved (as in the or operation), but are ranked by their relevance measure To use term frequency for ranking, the index structure should additionally maintain the number of times terms occur in each document To reduce this effort, they may use a compressed representation with only a few bits, which approximates the term frequency The index should also store the document frequency of each term (that is, the number of documents in which the term appears)
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2253 Measuring Retrieval Effectiveness
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Each keyword may be contained in a large number of documents; hence, a compact representation is critical to keep space usage of the index low Thus, the sets of documents for a keyword are maintained in a compressed form So that storage space is saved, the index is sometimes stored such that the retrieval is approximate; a few relevant documents may not be retrieved (called a false drop or false negative), or a few irrelevant documents may be retrieved (called a false positive) A good index structure will not have any false drops, but may permit a few false positives; the system can lter them away later by looking at the keywords that they actually contain In Web indexing, false positives are not desirable either, since the actual document may not be quickly accessible for ltering Two metrics are used to measure how well an information-retrieval system is able to answer queries The rst, precision, measures what percentage of the retrieved documents are actually relevant to the query The second, recall, measures what percentage of the documents relevant to the query were retrieved Ideally both should be 100 percent Precision and recall are also important measures for understanding how well a particular document ranking strategy performs Ranking strategies can result in false negatives and false positives, but in a more subtle sense False negatives may occur when documents are ranked, because relevant documents get low rankings; if we fetched all documents down to documents with very low ranking there would be very few false negatives However, humans would rarely look beyond the rst few tens of returned documents, and may thus miss relevant documents because they are not ranked among the top few Exactly what is a false negative depends on how many documents are examined Therefore instead of having a single number as the measure of recall, we can measure the recall as a function of the number of documents fetched
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Silberschatz Korth Sudarshan: Database System Concepts, Fourth Edition
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22 Advanced Querying and Information Retrieval
The McGraw Hill Companies, 2001
Information-Retrieval Systems
False positives may occur because irrelevant documents get higher rankings than relevant documents This too depends on how many documents are examined One option is to measure precision as a function of number of documents fetched A better and more intuitive alternative for measuring precision is to measure it as a function of recall With this combined measure, both precision and recall can be computed as a function of number of documents, if required For instance, we can say that with a recall of 50 percent the precision was 75 percent, whereas at a recall of 75 percent the precision dropped to 60 percent In general, we can draw a graph relating precision to recall These measures can be computed for individual queries, then averaged out across a suite of queries in a query benchmark Yet another problem with measuring precision and recall lies in how to de ne which documents are really relevant and which are not In fact, it requires understanding of natural language, and understanding of the intent of the query, to decide if a document is relevant or not Researchers therefore have created collections of documents and queries, and have manually tagged documents as relevant or irrelevant to the queries Different ranking systems can be run on these collections to measure their average precision and recall across multiple queries
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