Object identification is a crucial step in most information systems. Nowadays, we have many different ways to identify entities such as surrogates, keys and object identifiers. However, not all of them guarantee the entity identity. Many works have been introduced in the literature for discovering meaningful IDs, but all of them work at the logical or data level and they share some constraints inherent to the kind of approach. Addressing it at the logical level, we may miss some important data dependencies, while the cost to identify data dependencies at the data level may not be affordable. In this paper, we propose an approach for discovering fact IDs from domain ontologies. In our approach, we guide the process at the conceptual level and we introduce a set of pruning rules for improving the performance by reducing the number of ID hypotheses generated and to be verified with data. Finally, we also introduce a simulation over a case study to show the feasibility of our method
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