Abstract
An important service for systems providing access to information is the organization of returned search results. Vector model search results may be represented by a sphere in an n-dimensional space. A query represents the center of this sphere whose size is determined by its radius or by the amount of documents it contains. The goal of searching is to have all documents relevant to a query present within this sphere. It is known that not all relevant documents are present in this sphere and that is why various methods for improving search results, which can be implemented on the basis of expanding the original question, have been developed. Our goal is to utilize knowledge of document similarity contained in textual databases to obtain a larger amount of relevant documents while minimizing those cancelled due to their irrelevance. In the article we will define the concept k-path (topical development). For the individual development of vector query results, we will propose the SORT-EACH algorithm, which uses the aforementioned methods for acquiring topical development.
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Martinovič, J., Snášel, V., Dvorský, J., Dráždilová, P. (2010). Search in Documents Based on Topical Development. In: Snášel, V., Szczepaniak, P.S., Abraham, A., Kacprzyk, J. (eds) Advances in Intelligent Web Mastering - 2. Advances in Intelligent and Soft Computing, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10687-3_15
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DOI: https://doi.org/10.1007/978-3-642-10687-3_15
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