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

Alharbi, HYM; Saraee, MH

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.

Citation

Alharbi, H., & Saraee, M. (2016, July). Semantic aware Bayesian network model for actionable knowledge discovery in linked data. Presented at 12th International Conference, MLDM 2016, New York, NY, USA

• Presentation Conference Type: Other
• Conference Name: 12th International Conference, MLDM 2016
• Conference Location: New York, NY, USA
• Start Date: Jul 16, 2016
• End Date: Jul 21, 2016
• Online Publication Date: Jun 28, 2016
• Publication Date: Jun 28, 2016
• Deposit Date: Jul 14, 2017
• Publicly Available Date: Jul 14, 2017
• Series Title: Lecture Notes in Computer Science
• Series Number: 9729
• Book Title: Machine Learning and Data Mining in Pattern Recognition
• ISBN: 9783319419190;-9783319419206
• DOI: https://doi.org/10.1007/978-3-319-41920-6_11
• Publisher URL: http://dx.doi.org/10.1007/978-3-319-41920-6_11
• Related Public URLs: https://link.springer.com/book/10.1007/978-3-319-41920-6
• Additional Information: Event Type : Conference