xCrawl: a high-recall crawling method for Web mining

Web mining systems exploit the redundancy of data published on the Web to automatically extract information from existing Web documents. The first step in the Information Extraction process is thus to locate as many Web pages as possible that contain relevant information within a limited period of t...

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Vydáno v:	Knowledge and information systems Ročník 25; číslo 2; s. 303 - 326
Hlavní autoři:	Shchekotykhin, Kostyantyn, Jannach, Dietmar, Friedrich, Gerhard
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer-Verlag 01.11.2010 Springer Springer Nature B.V
Témata:	Algorithms Analysis Applied sciences Automation Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Data mining Data Mining and Knowledge Discovery Data processing. List processing. Character string processing Database Management Descriptions Digital cameras Exact sciences and technology Extraction Hierarchies Information retrieval Information Storage and Retrieval Information systems Information Systems and Communication Service Information Systems Applications (incl.Internet) Information systems. Data bases IT in Business Memory organisation. Data processing Mining Recall Regular Paper Search engines Searches Software Studies URLs Websites Information retrieval Web crawling Information extraction Web mining Data analysis Extraction process Redundancy Electronic document Data mining Information browsing World wide web Automatic generation Internet Web site
ISSN:	0219-1377, 0219-3116
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Abstract	Web mining systems exploit the redundancy of data published on the Web to automatically extract information from existing Web documents. The first step in the Information Extraction process is thus to locate as many Web pages as possible that contain relevant information within a limited period of time, a task which is commonly accomplished by applying focused crawling techniques. The performance of such a crawler can be measured by its “recall”, i.e., the percentage of documents found and identified as relevant compared to the total number of existing documents. A higher recall value implies that more redundant data are available, which in turn leads to better results in the subsequent fact extraction phase of the Web mining process. In this paper, we propose xCrawl , a new focused crawling method which outperforms state-of-the-art approaches with respect to the recall values achievable within a given period of time. This method is based on a new combination of ideas and techniques used to identify and exploit the navigational structures of Web sites, such as hierarchies, lists, or maps. In addition, automatic query generation is applied to rapidly collect Web sources containing target documents. The proposed crawling technique was inspired by the requirements of a Web mining system developed to extract product and service descriptions given in tabular form and was evaluated in different application scenarios. Comparisons with existing focused crawling techniques reveal that the new crawling method leads to a significant increase in recall while maintaining precision.
AbstractList	Issue Title: Special Issue:Best Papers from the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2008);Guest Editors: Takashi Washio, Einoshin Suzuki and Kai Ming Ting Web mining systems exploit the redundancy of data published on the Web to automatically extract information from existing Web documents. The first step in the Information Extraction process is thus to locate as many Web pages as possible that contain relevant information within a limited period of time, a task which is commonly accomplished by applying focused crawling techniques. The performance of such a crawler can be measured by its "recall", i.e., the percentage of documents found and identified as relevant compared to the total number of existing documents. A higher recall value implies that more redundant data are available, which in turn leads to better results in the subsequent fact extraction phase of the Web mining process. In this paper, we propose xCrawl, a new focused crawling method which outperforms state-of-the-art approaches with respect to the recall values achievable within a given period of time. This method is based on a new combination of ideas and techniques used to identify and exploit the navigational structures of Web sites, such as hierarchies, lists, or maps. In addition, automatic query generation is applied to rapidly collect Web sources containing target documents. The proposed crawling technique was inspired by the requirements of a Web mining system developed to extract product and service descriptions given in tabular form and was evaluated in different application scenarios. Comparisons with existing focused crawling techniques reveal that the new crawling method leads to a significant increase in recall while maintaining precision.[PUBLICATION ABSTRACT] Web mining systems exploit the redundancy of data published on the Web to automatically extract information from existing Web documents. The first step in the Information Extraction process is thus to locate as many Web pages as possible that contain relevant information within a limited period of time, a task which is commonly accomplished by applying focused crawling techniques. The performance of such a crawler can be measured by its "recall", i.e., the percentage of documents found and identified as relevant compared to the total number of existing documents. A higher recall value implies that more redundant data are available, which in turn leads to better results in the subsequent fact extraction phase of the Web mining process. In this paper, we propose xCrawl, a new focused crawling method which outperforms state-of-the-art approaches with respect to the recall values achievable within a given period of time. This method is based on a new combination of ideas and techniques used to identify and exploit the navigational structures of Web sites, such as hierarchies, lists, or maps. In addition, automatic query generation is applied to rapidly collect Web sources containing target documents. The proposed crawling technique was inspired by the requirements of a Web mining system developed to extract product and service descriptions given in tabular form and was evaluated in different application scenarios. Comparisons with existing focused crawling techniques reveal that the new crawling method leads to a significant increase in recall while maintaining precision. Web mining systems exploit the redundancy of data published on the Web to automatically extract information from existing Web documents. The first step in the Information Extraction process is thus to locate as many Web pages as possible that contain relevant information within a limited period of time, a task which is commonly accomplished by applying focused crawling techniques. The performance of such a crawler can be measured by its “recall”, i.e., the percentage of documents found and identified as relevant compared to the total number of existing documents. A higher recall value implies that more redundant data are available, which in turn leads to better results in the subsequent fact extraction phase of the Web mining process. In this paper, we propose xCrawl , a new focused crawling method which outperforms state-of-the-art approaches with respect to the recall values achievable within a given period of time. This method is based on a new combination of ideas and techniques used to identify and exploit the navigational structures of Web sites, such as hierarchies, lists, or maps. In addition, automatic query generation is applied to rapidly collect Web sources containing target documents. The proposed crawling technique was inspired by the requirements of a Web mining system developed to extract product and service descriptions given in tabular form and was evaluated in different application scenarios. Comparisons with existing focused crawling techniques reveal that the new crawling method leads to a significant increase in recall while maintaining precision.
Author	Shchekotykhin, Kostyantyn Friedrich, Gerhard Jannach, Dietmar
Author_xml	– sequence: 1 givenname: Kostyantyn surname: Shchekotykhin fullname: Shchekotykhin, Kostyantyn email: kostya@ifit.uni-klu.ac.at organization: University Klagenfurt – sequence: 2 givenname: Dietmar surname: Jannach fullname: Jannach, Dietmar organization: Technische Universität Dortmund – sequence: 3 givenname: Gerhard surname: Friedrich fullname: Friedrich, Gerhard organization: University Klagenfurt
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Cites_doi	10.1007/s10115-008-0152-4 10.1108/eb026866 10.1007/s10115-007-0094-2 10.1016/S0169-7552(98)00108-1 10.1145/1142473.1142504 10.1016/S0004-3702(00)00004-7 10.1016/S1389-1286(99)00052-3 10.2753/JEC1086-4415110201 10.1145/383952.383995 10.1109/ICWE.2008.24 10.1145/1298406.1298438 10.1109/TKDE.2003.1208999 10.1109/TKDE.2004.1264823 10.1145/324133.324140 10.1145/1242572.1242630 10.1007/s10115-007-0107-1 10.1007/3-540-48686-0_1 10.1145/775152.775178 10.1016/j.websem.2009.04.002
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Snippet	Web mining systems exploit the redundancy of data published on the Web to automatically extract information from existing Web documents. The first step in the... Issue Title: Special Issue:Best Papers from the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2008);Guest Editors: Takashi Washio,...
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SubjectTerms	Algorithms Analysis Applied sciences Automation Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Data mining Data Mining and Knowledge Discovery Data processing. List processing. Character string processing Database Management Descriptions Digital cameras Exact sciences and technology Extraction Hierarchies Information retrieval Information Storage and Retrieval Information systems Information Systems and Communication Service Information Systems Applications (incl.Internet) Information systems. Data bases IT in Business Memory organisation. Data processing Mining Recall Regular Paper Search engines Searches Software Studies URLs Websites
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Title	xCrawl: a high-recall crawling method for Web mining
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