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Information retrieval with concept discovery in digital collections for agriculture and natural resources.

纪录类型:	书目-语言数据,印刷品 : Monograph/item
[NT 47271] Title/Author:	Information retrieval with concept discovery in digital collections for agriculture and natural resources.
作者:	Ziemba, Lukasz.
面页册数:	172 p.
附注:	Source: Dissertation Abstracts International, Volume: 72-10, Section: A, page: 3565.
Contained By:	Dissertation Abstracts International72-10A.
标题:	Language, Linguistics.
标题:	Engineering, Agricultural.
标题:	Information Science.
ISBN:	9781124795324
[NT 15000229] null:	The amount and complexity of information available in a digital form is already huge and new information is being produced every day. Retrieving information relevant to address a particular need becomes a significant issue. This work utilizes knowledge organization systems (KOS), such as thesauri and ontologies and applies information extraction (IE) and computational linguistics (CL) techniques to organize, manage and retrieve information stored in digital collections in the agricultural domain. Two real world applications of the approach have been developed and are available and actively used by the public.
电子资源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3467600

Information retrieval with concept discovery in digital collections for agriculture and natural resources.
Ziemba, Lukasz.

Information retrieval with concept discovery in digital collections for agriculture and natural resources. - 172 p.

Source: Dissertation Abstracts International, Volume: 72-10, Section: A, page: 3565.

Thesis (Ph.D.)--University of Florida, 2011.

The amount and complexity of information available in a digital form is already huge and new information is being produced every day. Retrieving information relevant to address a particular need becomes a significant issue. This work utilizes knowledge organization systems (KOS), such as thesauri and ontologies and applies information extraction (IE) and computational linguistics (CL) techniques to organize, manage and retrieve information stored in digital collections in the agricultural domain. Two real world applications of the approach have been developed and are available and actively used by the public.

ISBN: 9781124795324Subjects--Topical Terms:

423211
Language, Linguistics.

Information retrieval with concept discovery in digital collections for agriculture and natural resources.
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245 1 0 $a Information retrieval with concept discovery in digital collections for agriculture and natural resources.
300 $a 172 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-10, Section: A, page: 3565.
500 $a Adviser: Howard Beck.
502 $a Thesis (Ph.D.)--University of Florida, 2011.
520 $a The amount and complexity of information available in a digital form is already huge and new information is being produced every day. Retrieving information relevant to address a particular need becomes a significant issue. This work utilizes knowledge organization systems (KOS), such as thesauri and ontologies and applies information extraction (IE) and computational linguistics (CL) techniques to organize, manage and retrieve information stored in digital collections in the agricultural domain. Two real world applications of the approach have been developed and are available and actively used by the public.
520 $a An ontology is used to manage the Water Conservation Digital Library holding a dynamic collection of various types of digital resources in the domain of urban water conservation in Florida, USA. The ontology based back-end powers a fully operational web interface, available at http://library.conservefloridawater.org. The system has demonstrated numerous benefits of the ontology application, including accurate retrieval of resources, information sharing and reuse, and has proved to effectively facilitate information management. The major difficulty encountered with the approach is that large and dynamic number of concepts makes it difficult to keep the ontology consistent and to accurately catalog resources manually.
520 $a To address the aforementioned issues, a combination of IE and CL techniques, such as Vector Space Model and probabilistic parsing, with the use of Agricultural Thesaurus were adapted to automatically extract concepts important for each of the texts in the Best Management Practices (BMP) Publication Library---a collection of documents in the domain of agricultural BMPs in Florida available at http://lyra.ifas.ufl.edu/LIB. A new approach of domain-specific concept discovery with the use of Internet search engine was developed. Initial evaluation of the results indicates significant improvement in precision of information extraction.
520 $a The approach presented in this work focuses on problems unique to agriculture and natural resources domain, such as domain specific concepts and vocabularies, but should be applicable to any collection of texts in digital format. It may be of potential interest for anyone who needs to effectively manage a collection of digital resources.
590 $a School code: 0070.
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650 4 $a Engineering, Agricultural. $3 423021
650 4 $a Information Science. $3 422941
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790 1 0 $a Beck, Howard, $e advisor
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791 $a Ph.D.
792 $a 2011
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