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An entropy-based class assignment detection approach for RDF data

Citation

Barati, M and Bai, Q and Liu, Q, An entropy-based class assignment detection approach for RDF data, Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence. Part II. Lecture Notes in Computer Science, volume 11013, 28-31 August 2018, Nanjing, China, pp. 412-420. ISBN 9783319973098 (2018) [Refereed Conference Paper]


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Copyright 2018 Springer

DOI: doi: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 Details

Item Type:Refereed Conference Paper
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Pattern recognition
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:140679
Year Published:2018
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2020-09-01
Last Modified:2020-10-29
Downloads:0

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