Semantic aware Bayesian network model for actionable knowledge discovery in linked data

Alharbi, HYM and Saraee, MH 2016, 'Semantic aware Bayesian network model for actionable knowledge discovery in linked data' , Machine Learning and Data Mining in Pattern Recognition, 9729 , pp. 143-154.

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Abstract

The majority of the conventional mining algorithms treat the mining process as an isolated data-driven procedure and overlook the semantic of the targeted data. As a result, the generated patterns are abundant and end users cannot act upon them seamlessly. Furthermore, interdisciplinary knowledge can not be obtained from domain-specific silo of data. The emergence of Linked Data (LD) as a new model for knowledge representation, which intertwines data with its semantics, has introduced new opportunities for data miners. Accordingly, this paper proposes an ontology-based Semantic-Aware Bayesian network (BN) model. In contrast to the existing mining algorithms, the proposed model does into transform the original format of the LD set. Therefore, it not only accommodates the semantic aspects in LD, but also caters to the need of connecting different data-sets from different domains. We evaluate the proposed model on a Bone Dysplasia dataset, Experimental results show promising performance.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Machine Learning and Data Mining in Pattern Recognition
Publisher: Springer
ISSN: 0302-9743
Related URLs:
Depositing User: Dr Mo Saraee
Date Deposited: 14 Jul 2017 08:42
Last Modified: 08 Aug 2017 22:50
URI: http://usir.salford.ac.uk/id/eprint/43009

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