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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Veröffentlicht in:Knowledge and information systems Jg. 25; H. 2; S. 303 - 326
Hauptverfasser: Shchekotykhin, Kostyantyn, Jannach, Dietmar, Friedrich, Gerhard
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer-Verlag 01.11.2010
Springer
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
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ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-009-0266-3