Abstract
Data quality, completeness, and consistency are crucial for simulation modeling and predictive tasks in socioeconomic systems. Such systems involve heterogeneous entities and their interrelations, which are becoming available only by combining various data sources. In this study, three different sources were combined in a single knowledge graph (KG). It includes an online social network, an online recruitment system, and a financial bank. The constructed knowledge graph is evaluated on link prediction tasks to obtain complete and consistent data. We try to reconstruct links between users and a) socioeconomic statuses, b) organizations they work for, c) job positions. Knowledge graph embedding models and a graph neural network based on Transformer architecture were applied. We get promising results for reconstruction of User-Employer relations (\(MRR=0.42\), \(Hits@10=0.74\)), as well as for reconstruction of User-Position relations (\(MRR=0.59\), \(Hits@10=0.88\)).
The reported study was funded by RFBR according to the research project â„–Â 20-37-90126. We are grateful to Artem Petrov for his assistance with text processing tasks and Valentina Guleva for her valuable scientific advice.
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Notes
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We use \(community\_detection\) method from https://github.com/UKPLab/sentence-transformers.
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Kalinin, A., Vaganov, D., Shikov, E. (2022). Relations Reconstruction in a Knowledge Graph of a Socioeconomic System. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13088. Springer, Cham. https://doi.org/10.1007/978-3-030-95408-6_12
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