Rule Identification from Web Pages by the XRML Approach

In the world of Web pages, there are oceans of documents in natural language texts and tables. To extract rules from Web pages and maintain consistency between them, we have developed the framework of XRML (eXtensible Rule Markup Language). XRML allows the identification of rules on Web pages and ge...

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Main Authors: KANG, Juyoung, LEE, Jae Kyu
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Language:English
Published: Institutional Knowledge at Singapore Management University 2005
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Online Access:https://ink.library.smu.edu.sg/sis_research/1181
http://dx.doi.org/10.1016/j.dss.2005.01.004
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spelling sg-smu-ink.sis_research-21802010-12-22T08:24:06Z Rule Identification from Web Pages by the XRML Approach KANG, Juyoung LEE, Jae Kyu In the world of Web pages, there are oceans of documents in natural language texts and tables. To extract rules from Web pages and maintain consistency between them, we have developed the framework of XRML (eXtensible Rule Markup Language). XRML allows the identification of rules on Web pages and generates the identified rules automatically. For this purpose, we have designed the Rule Identification Markup Language (RIML), which is similar to the formal Rule Structure Markup Language (RSML), both as parts of XRML. RIML 2.0 is designed to identify rules not only from texts, but also from tables on Web pages, and to transform to the formal rules in RSML syntax automatically. While designing RIML 2.0, we considered the features of sharing variables and values, omitted terms, and synonyms. We have conducted an experiment to evaluate the potential benefit of the XRML approach with real world Web pages of Amazon.com, BarnesandNoble.com, and Powells.com. We found that 100.0% of the rules and 99.7% of the rule components could be identified and automatically generated if we do not count the statements for linkages, which generically do not exist on the Web pages. Since the linkage components occupy 11.2% of all components in the rule base, the overall limitation of automatic rule generation is 88.8%. In this setting, 88.5% of the overall rule components could be generated from the identified rules from the Web pages. The result provides solid proof that XRML can facilitate the extraction and maintenance of rules from Web pages while building expert systems in the Semantic Web environment. 2005-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/1181 info:doi/10.1016/j.dss.2005.01.004 http://dx.doi.org/10.1016/j.dss.2005.01.004 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Rule identification Rule acquisition Knowledge engineering Knowledge acquisition XRML RuleML XML Computer Sciences Management Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Rule identification
Rule acquisition
Knowledge engineering
Knowledge acquisition
XRML
RuleML
XML
Computer Sciences
Management Information Systems
spellingShingle Rule identification
Rule acquisition
Knowledge engineering
Knowledge acquisition
XRML
RuleML
XML
Computer Sciences
Management Information Systems
KANG, Juyoung
LEE, Jae Kyu
Rule Identification from Web Pages by the XRML Approach
description In the world of Web pages, there are oceans of documents in natural language texts and tables. To extract rules from Web pages and maintain consistency between them, we have developed the framework of XRML (eXtensible Rule Markup Language). XRML allows the identification of rules on Web pages and generates the identified rules automatically. For this purpose, we have designed the Rule Identification Markup Language (RIML), which is similar to the formal Rule Structure Markup Language (RSML), both as parts of XRML. RIML 2.0 is designed to identify rules not only from texts, but also from tables on Web pages, and to transform to the formal rules in RSML syntax automatically. While designing RIML 2.0, we considered the features of sharing variables and values, omitted terms, and synonyms. We have conducted an experiment to evaluate the potential benefit of the XRML approach with real world Web pages of Amazon.com, BarnesandNoble.com, and Powells.com. We found that 100.0% of the rules and 99.7% of the rule components could be identified and automatically generated if we do not count the statements for linkages, which generically do not exist on the Web pages. Since the linkage components occupy 11.2% of all components in the rule base, the overall limitation of automatic rule generation is 88.8%. In this setting, 88.5% of the overall rule components could be generated from the identified rules from the Web pages. The result provides solid proof that XRML can facilitate the extraction and maintenance of rules from Web pages while building expert systems in the Semantic Web environment.
format text
author KANG, Juyoung
LEE, Jae Kyu
author_facet KANG, Juyoung
LEE, Jae Kyu
author_sort KANG, Juyoung
title Rule Identification from Web Pages by the XRML Approach
title_short Rule Identification from Web Pages by the XRML Approach
title_full Rule Identification from Web Pages by the XRML Approach
title_fullStr Rule Identification from Web Pages by the XRML Approach
title_full_unstemmed Rule Identification from Web Pages by the XRML Approach
title_sort rule identification from web pages by the xrml approach
publisher Institutional Knowledge at Singapore Management University
publishDate 2005
url https://ink.library.smu.edu.sg/sis_research/1181
http://dx.doi.org/10.1016/j.dss.2005.01.004
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