A Survey: Soft Computing in Intelligent Information Retrieval Systems

The paper is devoted to the problems of orientation and navigation in the world of verbal presentation of scientific knowledge. The solution of these problems is currently hampered by the lack of intelligent information retrieval systems that allow comparing descriptions of various scientific works at the level of coincidence of semantic situations, rather than keywords. The article discusses methods for the formation and recognition of semantic images of scientific publications belonging to specific subject areas. The method for constructing a semantic image of a scientific text developed by Iuliia Bruttan allows to form an image of the text of a scientific publication, which can be used as input data for a neural network. Training of this neural network will automate the processes of pattern recognition and classification of scientific publications according to specified criteria. The approaches to the recognition of semantic images of scientific publications based on neural networks considered in the paper can be used to organize the semantic search for scientific publications, as well as in the design of intelligent information retrieval systems.

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Special issue on soft computing applications to intelligent information retrieval on the Internet

International Journal of Approximate Reasoning ◽

10.1016/j.ijar.2003.07.001 ◽

2003 ◽

Vol 34 (2-3) ◽

pp. 89-95 ◽

Cited By ~ 8

Author(s):

O. Cordón ◽

E. Herrera-Viedma

Keyword(s):

Information Retrieval ◽

Soft Computing ◽

The Internet ◽

Special Issue ◽

Intelligent Information Retrieval ◽

Intelligent Information

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Relevance Predictability in Information Retrieval Systems

Methods of Information in Medicine ◽

10.1055/s-0038-1636254 ◽

1967 ◽

Vol 06 (02) ◽

pp. 45-51 ◽

Cited By ~ 6

Author(s):

A. Kent ◽

J. Belzer ◽

M. Kuhfeerst ◽

E. D. Dym ◽

D. L. Shirey ◽

...

Keyword(s):

Information Retrieval ◽

Experimental Conditions ◽

Treatment Groups ◽

Retrieval Systems ◽

Significant Difference ◽

Information Retrieval Systems ◽

High Predictability ◽

Intermediate Response ◽

Quantitative Indicators ◽

Level Of Processing

An experiment is described which attempts to derive quantitative indicators regarding the potential relevance predictability of the intermediate stimuli used to represent documents in information retrieval systems. In effect, since the decision to peruse an entire document is often predicated upon the examination of one »level of processing« of the document (e.g., the citation and/or abstract), it became interesting to analyze the properties of what constitutes »relevance«. However, prior to such an analysis, an even more elementary step had to be made, namely, to determine what portions of a document should be examined.An evaluation of the ability of intermediate response products (IRPs), functioning as cues to the information content of full documents, to predict the relevance determination that would be subsequently made on these documents by motivated users of information retrieval systems, was made under controlled experimental conditions. The hypothesis that there might be other intermediate response products (selected extracts from the document, i.e., first paragraph, last paragraph, and the combination of first and last paragraph), that would be as representative of the full document as the traditional IRPs (citation and abstract) was tested systematically. The results showed that:1. there is no significant difference among the several IRP treatment groups on the number of cue evaluations of relevancy which match the subsequent user relevancy decision on the document;2. first and last paragraph combinations have consistently predicted relevancy to a higher degree than the other IRPs;3. abstracts were undistinguished as predictors; and4. the apparent high predictability rating for citations was not substantive.Some of these results are quite different than would be expected from previous work with unmotivated subjects.

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