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Information Retrieval over Uncertain...

Xu, Jie.

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Information Retrieval over Uncertain Data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Information Retrieval over Uncertain Data./
作者:	Xu, Jie.
面頁冊數:	176 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
Contained By:	Dissertation Abstracts International75-10B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3627302
ISBN:	9781321024593

Information Retrieval over Uncertain Data.
Xu, Jie.

Information Retrieval over Uncertain Data. - 176 p.

Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.

Thesis (Ph.D.)--University of California, Irvine, 2014.

Increasingly, end-user data published on web or stored in databases is generated using variety of automated data processing techniques, such as automatic image tagging, speech recognition, entity resolution, etc. Such techniques often employ probabilistic models and generate uncertain data with probabilistic attributes. Typical information retrieval tasks consists of retrieving specific information or extracting an information overview from data. Uncertainty existing in the data complicates such information retrieval tasks.

ISBN: 9781321024593Subjects--Topical Terms:

626642
Computer Science.

Information Retrieval over Uncertain Data.
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245 1 0 $a Information Retrieval over Uncertain Data.
300 $a 176 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
500 $a Advisers: Sharad Mehrotra; Dmitri V. Kalashnikov.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2014.
520 $a Increasingly, end-user data published on web or stored in databases is generated using variety of automated data processing techniques, such as automatic image tagging, speech recognition, entity resolution, etc. Such techniques often employ probabilistic models and generate uncertain data with probabilistic attributes. Typical information retrieval tasks consists of retrieving specific information or extracting an information overview from data. Uncertainty existing in the data complicates such information retrieval tasks.
520 $a In this thesis, we explore various aspects of the problem of information retrieval on top of uncertain data. One such aspect is to reduce the uncertainty of data itself such that the quality of the end application on top of the data can be improved. We explore how knowledge of external domain semantics can be utilized to improve the data quality. We apply our technique to the problem of speech annotation of images and use that as an example to show the advantage of such a semantics-based approach.
520 $a While uncertainty reduction techniques we design can bring additional determinism to the data, usually it is infeasible to completely eliminate the uncertainty without information loss. As a result, users or end applications of information retrieval have to deal with certain level of uncertainty. Requiring user/end application to handle such uncertainty complicates application design and may not be feasible in certain scenario. For instance, uncertain data has to be stored in legacy systems (such as Flickr) which do not understand probabilistic input. The second aspect is the problem of the determinization of uncertain data where the goal is to the select best deterministic representation of uncertain data based on application criteria.
520 $a Finally, another aspect of the studied problem is that of extracting information overview in the form of summary from uncertain data. Summarization is often used in the context of information retrieval to overcome the problem of information overload. The thesis explores how the uncertainty and heterogeneity can be overcome in generating summaries.
590 $a School code: 0030.
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