A Task-specific Approach for Crawling the Deep Web.
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| Title: | A Task-specific Approach for Crawling the Deep Web. |
|---|---|
| Authors: | Álvarez, Manuel1 mad@udc.es, Raposo, Juan1 jrs@udc.es, Cacheda, Fidel1 fidel@udc.es, Pan, Alberto1 apan@udc.es |
| Source: | Engineering Letters. 2006, Vol. 13 Issue 3, p204-215. 12p. |
| Subjects: | Prototypes, Engineering databases, Information storage & retrieval systems, ENGINE (Information retrieval system), Engineering |
| Abstract: | There is a great amount of valuable information on the web that cannot be accessed by conventional crawler engines. This portion of the web is usually known as the Deep Web or the Hidden Web. Most probably, the information of highest value contained in the deep web, is that behind web forms. In this paper, we describe a prototype hidden-web crawler able to access such content. Our approach is based on providing the crawler with a set of domain definitions, each one describing a specific data-collecting task. The crawler uses these descriptions to identify relevant query forms and to learn to execute queries on them. We have tested our techniques for several real world tasks, obtaining a high degree of effectiveness. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 24035573 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Task-specific Approach for Crawling the Deep Web. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Álvarez%2C+Manuel%22">Álvarez, Manuel</searchLink><relatesTo>1</relatesTo><i> mad@udc.es</i><br /><searchLink fieldCode="AR" term="%22Raposo%2C+Juan%22">Raposo, Juan</searchLink><relatesTo>1</relatesTo><i> jrs@udc.es</i><br /><searchLink fieldCode="AR" term="%22Cacheda%2C+Fidel%22">Cacheda, Fidel</searchLink><relatesTo>1</relatesTo><i> fidel@udc.es</i><br /><searchLink fieldCode="AR" term="%22Pan%2C+Alberto%22">Pan, Alberto</searchLink><relatesTo>1</relatesTo><i> apan@udc.es</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. 2006, Vol. 13 Issue 3, p204-215. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Prototypes%22">Prototypes</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+databases%22">Engineering databases</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22ENGINE+%28Information+retrieval+system%29%22">ENGINE (Information retrieval system)</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering%22">Engineering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: There is a great amount of valuable information on the web that cannot be accessed by conventional crawler engines. This portion of the web is usually known as the Deep Web or the Hidden Web. Most probably, the information of highest value contained in the deep web, is that behind web forms. In this paper, we describe a prototype hidden-web crawler able to access such content. Our approach is based on providing the crawler with a set of domain definitions, each one describing a specific data-collecting task. The crawler uses these descriptions to identify relevant query forms and to learn to execute queries on them. We have tested our techniques for several real world tasks, obtaining a high degree of effectiveness. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 204 Subjects: – SubjectFull: Prototypes Type: general – SubjectFull: Engineering databases Type: general – SubjectFull: Information storage & retrieval systems Type: general – SubjectFull: ENGINE (Information retrieval system) Type: general – SubjectFull: Engineering Type: general Titles: – TitleFull: A Task-specific Approach for Crawling the Deep Web. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Álvarez, Manuel – PersonEntity: Name: NameFull: Raposo, Juan – PersonEntity: Name: NameFull: Cacheda, Fidel – PersonEntity: Name: NameFull: Pan, Alberto IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2006 Type: published Y: 2006 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 13 – Type: issue Value: 3 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
