Automatic Rule Extraction from Frequent Rules In Similar Network Sites
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-3 | ||
| Year of Publication : 2013 | ||
| Authors :Aakanksha Singh, B.Muruganantham |
Aakanksha Singh, B.Muruganantham "Automatic Rule Extraction from Frequent Rules In Similar Network Sites"International Journal of Computer Trends and Technology (IJCTT),V4(3):292-295 Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - Knowledge is an essential part of most Semantic Web applications and ontology. Ontology is the most important part of the knowledge. Ontology learning, which refers to extracting conceptual knowledge from several sources and building ontology from scratch, enriching, or adapting an existing ontology is one of the attempts at knowledge acquisition. Ontology is not sufficient to represent inferential knowledge. The ontology-based analysis with description logic is a popular issue of the Semantic Web. Rules are obtained from several sites of the same domain. The existing system has some problems in rule extracting. First, the web pages are identified for rule components and for their types. Second thing is how to compose the rules with rule components. The domain has similar Web sites explaining similar rules from each other. It decreases the burden on the knowledge experts and domain experts. Our idea for solving these problems is using rules of similar sites in limited situations. The two main steps of rule acquisition, which consists of rule component identification such as variables and values in Web pages by using RuleToOnto and rule composition with the identified rule components. We performed experiments demonstrating that our ontology-based rule acquisition approach works in a real-world application.
References-
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[4] Philipp Cimiano, Johanna VÄolker, “Text2Onto A Framework for Ontology Learning and Data-driven Change Discovery,” Institute AIFB, University of Karlsruhe.
[5] Mark Craven a Dan DiPasquo a Dayne Freitag, “Learning to Construct Knowledge Bases from the World Wide Web,” Carnegie Mellon University, 5000 Pittsburgh, PA, 15213-3891, USA bJust Research4616 Henry Street, Pittsburgh, PA, 15213, USA.
Keywords— Rule Acquisition, Breath first Search, Rule ontology.