Entity Resolution has been an active research topic for the last three decades, with numerous algorithms proposed in the literature. However, putting them into practice is often a complex task that requires implementing, combining and configuring complementary individual algorithms into comprehensive end-to-end workflows. To facilitate this process, we are developing pyJedAI, a novel system that provides a unifying framework for any type of main works in the field (i.e., both unsupervised and learning-based ones). Our vision is to facilitate both novice and expert users to use and combine these algorithms through a series of principled approaches for automatically configuring and benchmarking end-to-end pipelines.
Self-configured Entity Resolution with pyJedAI / Efthymiou, V., Ioannou, E., Karvounis, M., Koubarakis, M., Maciejewski, J., Nikoletos, K., Papadakis, G., Skoutas, D., Velegrakis, Y., Zeakis, A.. - (2023), pp. 339-343. (2023 IEEE International Conference on Big Data, BigData 2023 ita 2023) [10.1109/BigData59044.2023.10386556].
Self-configured Entity Resolution with pyJedAI
Velegrakis Y.;
2023-01-01
Abstract
Entity Resolution has been an active research topic for the last three decades, with numerous algorithms proposed in the literature. However, putting them into practice is often a complex task that requires implementing, combining and configuring complementary individual algorithms into comprehensive end-to-end workflows. To facilitate this process, we are developing pyJedAI, a novel system that provides a unifying framework for any type of main works in the field (i.e., both unsupervised and learning-based ones). Our vision is to facilitate both novice and expert users to use and combine these algorithms through a series of principled approaches for automatically configuring and benchmarking end-to-end pipelines.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
