SEWEBAR-CMS: A System for Postprocessing Association Rule Models

RuleML-2010 Challenge.

计算机科学;

Tomáš Kliegr ; David Chudán ; Andrej Hazucha ; Jan Rauch

Others  :  http://ceur-ws.org/Vol-649/paper9.pdf
PID  :  42466

学科分类：计算机科学（综合）

来源: CEUR

【 摘 要 】

The principal problem of the association rule (AR) mining task is the selection of rules that might be interesting for the domain expert from the many rules typically generated by the software. SEWEBAR-CMS is a Joomla!-based Content Management System for post-processing AR models that supports the data analyst in this effort. The input for the system are AR models in GUHA-extended PMMLAR model and machine-readable background knowledge elicited from domain experts within the CMS. PMML and background knowledge are converted to auto-generated reports with XSLT2HTML transformation and presented as CMS documents. They are also semantized according to the Association Rule Mining Ontology, interlinked, and stored in an external Ontopia knowledge base, which uses the Topic Map semantic web formalism. Queries issued from the CMS against Ontopia in the tolog language are used to select discovered ARs that are in some interesting relationship (e.g. exception, confirmation) with the background knowledge. The data analyst presents the mining results to the domain expert through the analytical report that blends in query results with free text and fragments of the auto-generated reports.