Abstract
In this work we address the concept of semantic redundancy in linked datasets considering class assignment assertions. We discuss how redundancy can be evaluated as well as the relationship between redundancy and some class hierarchy aspects: number of classes, number of instances a class has, number of class descendants and class depth. Finally, we performed an evaluation on the DBpedia dataset using SPARQL queries for data redundancy checks. Results obtained from this evaluation suggest that the number of redundant class assignments increases when the number of classes is higher, for general classes, with more descendants and for those with more number of instances. In this evaluation we also observed some patterns that can be used to classify class assignments. These observations may be useful for linked data stakeholders to understand how different schemas are used within a dataset, detect errors and improve the mechanisms to generate linked data.
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Mendoza, L., Díaz, A. (2017). An Approach to Evaluate Class Assignment Semantic Redundancy on Linked Datasets. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig SIMBig 2015 2016. Communications in Computer and Information Science, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-319-55209-5_7
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DOI: https://doi.org/10.1007/978-3-319-55209-5_7
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