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 contraxt to the exisiting mining algorithms, the proposed model does nto transorm the original format of the LD set. Therefore, it not only accomodates the sematnic aspects in LD, but also caters to the need of connectign different data-sets from different domains. We evaluate the proposed model on a Bone Dysplasia dataset, Experimental results show promising perfomance.
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Alharbi, H., Saraee, M. (2016). Semantic Aware Bayesian Network Model for Actionable Knowledge Discovery in Linked Data. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_11
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DOI: https://doi.org/10.1007/978-3-319-41920-6_11
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