A Context-Independent Ontological Linked Data Alignment Approach to Instance Matching

Armando Barbosa, Ig I. Bittencourt, Sean W. Siqueira, Diego Dermeval and Nicholas J. T. Cruz
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Armando Barbosa: Federal University of Alagoas, Brazil
Ig I. Bittencourt: Federal University of Alagoas, Brazil
Sean W. Siqueira: Federal University of the State of Rio de Janeiro, Brazil
Diego Dermeval: Federal University of Alagoas, Brazil
Nicholas J. T. Cruz: Federal University of Alagoas, Brazil

International Journal on Semantic Web and Information Systems (IJSWIS), 2022, vol. 18, issue 1, 1-29

Abstract: Linking data by finding matching instances in different datasets requires considering many characteristics, such as structural heterogeneity, implicit knowledge, and URI (Uniform Resource Identifier)-oriented identification. The authors propose a context-independent approach to align Linked data through an alignment process based on the ontological model’s components and considering data’s multidimensionality. The researchers experimented with the proposed approach against two methods for aligning linked data in two datasets and evaluated precision, recall, and f-measure metrics. The authors also conducted a case study in a real scenario considering a Brazilian publication dataset on computers and education. This study’s results indicate that the proposed approach overcomes the other methods (regarding the precision, recall, and f-measure metrics), requiring less work when changing the dataset domain. This work’s main contributions include enabling real datasets to be semi-automatically linked, presenting an approach capable of calculating resource similarity.

Date: 2022
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