2019/10/22 by Ben McCamish, Christopher Buß, McCamish, Ben +6
Computer Science · #Advanced Database Systems and Queries #Bayesian Modeling and Causal Inference #Data Management and Algorithms #Databases (cs.DB) #FOS: Computer and information sciences #cs.DB
paper · pdf · doi:10.48550/arxiv.1910.10263
demonstration
arxiv created 2019/10/22 · openalex publication_date 2019/10/22 · arxiv updated 2019/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Users often have to integrate information about entities from multiple data sources. This task is challenging as each data source may represent information about the same entity in a distinct form, e.g., each data source may use a different name for the same person. Currently, data from different representations are translated into a unified one via lengthy and costly expert attention and tuning. Such methods cannot scale to the rapidly increasing number and variety of available data sources. We demonstrate ProgMap, a entity-matching framework in which data sources learn to collaborate and integrate information about entities on-demand and with minimal expert intervention. The data sources leverage user feedback to improve the accuracy of their collaboration and results. ProgMap also has techniques to reduce the amount of required user feedback to achieve effective matchings.