Information Retrieval

Grossman, David A.; Frieder, Ophir

doi:10.1007/978-1-4020-3005-5

Cited by 195 publications

(22 citation statements)

References 189 publications

(250 reference statements)

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“…Suppose one wants to search under classification and this term is used in several disciplines such as Philosophy, Biology, Zoology, and Library Science. In searching, if one clicks on the term, it may appear on the screen: Philosophy Biology Zoology Library Science 5, 6 8,1 10,1 13,5…”

Section: Internalmentioning

confidence: 99%

“…Meanwhile there is a lot of repetition in one engine, rather than other ones. Grossman and Frieder [30] reciting from [Kahle, 1998, Lawrence and Giles, 1998], say: It is estimated that the Web now contains more than twenty million different content areas-presented on more than 320 million web pages, and one million web-servers, and it is doubling every nine months.…”

Section: Externalmentioning

confidence: 99%

“…They, like many others, group the models in three, which are Boolean, Vector, and Probabilities. Grossman and Frieder [6]propose eight, under the title of Retrieval Strategies. They define Retrieval Strategies as a measure of similarity between a query and a document.…”

Section: Introductionmentioning

confidence: 99%

“…They define Retrieval Strategies as a measure of similarity between a query and a document. [7]. Their eight strategies are as follows: Vector Space Model; Probabilistic Retrieval Strategies; Inference Networks; Extended Boolean Retrieval; Latent Semantic Indexing; Neural Networks; Genetic Algorithms; and Fuzzy Set Retrieval, but they emphasize that most research literature focuses on four key retrieval strategies: Vector, Probabilistic, Boolean, and Fuzzy-set.…”

Section: Introductionmentioning

confidence: 99%

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Users Satisfaction Through Better Indexing

Araghi¹

2005

Cataloging & Classification Quarterly

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Classification and indexing are two main tools to organize information to serve the users. Information architecture is nothing more than to organize better to achieve this goal. Any user seeks easy access and speed to reach one's information needs. A classifier/indexer must interpret or estimate the users' need in the best possible terms. Ranking algorithms-such as Boolean, Vector, or others-is highly recommended and practiced. Some define Retrieval Strategies as a measure of similarity between a quarry and document. Relevance is a criterion for matching aboutness. Aboutness is a criterion for decision-making. Better indexing, as well as better classification, is a key to reaching the ultimate goal in record management. Some suggestions are made for those who create databases, provide information engines, or manage the information.

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Section: Internalmentioning

confidence: 99%

Section: Externalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Users Satisfaction Through Better Indexing

Araghi¹

2005

Cataloging & Classification Quarterly

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show abstract

“…The IDF value aims to give high values to "important" words while minimizing the score for trivial words [5]. This is done by assigning a value to the word that is inversely proportional to the amount of documents the word appears in.…”

Section: Document Division and Similarity Calculationmentioning

confidence: 99%