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An entropy-based class assignment detection approach for RDF data

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Barati, M, Bai, Q

and Liu, Q 2018 , 'An entropy-based class assignment detection approach for RDF data', in Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence. Part II. Lecture Notes in Computer Science, volume 11013 , Springer, pp. 412-420 , doi: 10.1007/978-3-319-97310-4_47.

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Official URL: https://dx.doi.org/10.1007/978-3-319-97310-4_47

Abstract

The RDF-style Knowledge Bases usually contain a certain level of noises known as Semantic Web data quality issues. This paper has introduced a new Semantic Web data quality issue called Incorrect Class Assignment problem that shows the incorrect assignment between instances in the instance-level and corresponding classes in an ontology. We have proposed an approach called CAD (Class Assignment Detector) to find the correctness and incorrectness of relationships between instances and classes by analyzing features of classes in an ontology. Initial experiments conducted on a dataset demonstrate the effectiveness of CAD.

Item Type:	Conference Publication
Authors/Creators:	Barati, M and Bai, Q and Liu, Q
Journal or Publication Title:	Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence. Part II. Lecture Notes in Computer Science, volume 11013
Publisher:	Springer
DOI / ID Number:	10.1007/978-3-319-97310-4_47
Copyright Information:	Copyright 2018 Springer
Item Statistics:	View statistics for this item

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