Deep Model-Based Security-Aware Entity Alignment Method for Edge-Specific Knowledge Graphs

Jongmo Kim, Kunyoung Kim, Mye Sohn and Gyudong Park
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Jongmo Kim: Department of Industrial Engineering, Sungkyunkwan University, Suwon 16419, Korea
Kunyoung Kim: Department of Industrial Engineering, Sungkyunkwan University, Suwon 16419, Korea
Mye Sohn: Department of Industrial Engineering, Sungkyunkwan University, Suwon 16419, Korea
Gyudong Park: 2nd R&D Institute, Agency for Defense Development, Seoul 05771, Korea

Sustainability, 2022, vol. 14, issue 14, 1-22

Abstract: This paper proposes a deep model-based entity alignment method for the edge-specific knowledge graphs (KGs) to resolve the semantic heterogeneity between the edge systems’ data. To do so, this paper first analyzes the edge-specific knowledge graphs (KGs) to find unique characteristics. The deep model-based entity alignment method is developed based on their unique characteristics. The proposed method performs the entity alignment using a graph which is not topological but data-centric, to reflect the characteristics of the edge-specific KGs, which are mainly composed of the instance entities rather than the conceptual entities. In addition, two deep models, namely BERT (bidirectional encoder representations from transformers) for the concept entities and GAN (generative adversarial networks) for the instance entities, are applied to model learning. By utilizing the deep models, neural network models that humans cannot interpret, it is possible to secure data on the edge systems. The two learning models trained separately are integrated using a graph-based deep learning model GCN (graph convolution network). Finally, the integrated deep model is utilized to align the entities in the edge-specific KGs. To demonstrate the superiority of the proposed method, we perform the experiment and evaluation compared to the state-of-the-art entity alignment methods with the two experimental datasets from DBpedia, YAGO, and wikidata. In the evaluation metrics of Hits@k, mean rank (MR), and mean reciprocal rank (MRR), the proposed method shows the best predictive and generalization performance for the KG entity alignment.

Keywords: edge computing; data privacy and security; entity alignment; knowledge graph; deep model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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