| Autor: |
Caron, Emiel, Ioannou, Ekaterini, Filipe, Joaquim, Smialek, Michal, Brodsky, Alexander, Hammoudi, Slimane |
| Přispěvatelé: |
Department of Management, Research Group: Information & Supply Chain Management |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Proceedings of the 23th International Conference on Enterprise Information Systems (ICEIS 2021), 1(23), 148-156 |
| Popis: |
Entity resolution in databases focuses on detecting and merging entities that refer to the same real-world object. Collective resolution is among the most prominent mechanisms suggested to address this challenge since the resolution decisions are not made independently, but are based on the available relationships within the data. In this paper, we introduce a novel resolution approach that combines the essence of collective resolution with rules and transformations among entity attributes and values. We illustrate how the approach’s parameters are optimized based on a global optimization algorithm, i.e., simulated annealing, and explain how this optimization is performed using a small training set. The quality of the approach is verified through an extensive experimental evaluation with 40M real-world scientific entities from the Patstat database. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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