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README.md

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 # SparkER
-_An Entity Resolution framework developed in Scala for Apache Spark._
+_An Entity Resolution framework developed in Scala and Python for Apache Spark._
+
+**Please note that the Scala version is not maintained anymore, only the Python one is kept updated.**
 
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@@ -24,13 +26,16 @@ If use this library, please cite:
 
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+### News
+* **2022-05-18**: we added the Generalized Supervised meta-blocking described in our new paper [6]. [Here](https://github.com/Gaglia88/sparker/blob/master/python/examples/Generalized%20Supervised%20Meta-blocking.ipynb) there is an example of usage.
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 ### Entity Resolution
 Entity Resolution (*ER*) is the task of identifying different records (a.k.a. entity *profiles*) that pertain to the same real-world entity. Comparing all the possible pairs of records in a data set may be very inefficient (quadratic complexity), in particular in the context of Big Data, e.g., when the records to compare are hundreds of millions. To reduce this complexity, usually ER uses different blocking techniques (e.g. token blocking, n-grams, etc.) to create clusters of profiles (called blocks). The goal of this process is to reduce the global number of comparisons, because will be compared only the records that are in the same blocks.
 
 Unfortunately, in the Big Data context the blocking techniques still produces too many comparisons to be managed in a reasonable time, to reduce more the number of comparison the meta-blocking techniques was introduced [2]. The idea is to create a graph using the information learned from the blocks: the profiles in the blocks represents the nodes of the graph, and the comparisons between them represents the edges. Then is possible to calculate some metrics on the graph and use them to pruning the less significant edges.
 
 ### Meta-Blocking for Spark
-SparkER implements for Spark the Meta-Blocking techniques described in Simonini et al. [1] and Papadakis et al. [2].
+SparkER implements for Spark the Meta-Blocking techniques described in Simonini et al. [1], Papadakis et al. [2], Gagliardelli et al. [6].
 
 [![stages](https://github.com/Gaglia88/sparker/raw/master/img/stages.png)](#stages)
 
@@ -59,3 +64,5 @@ For any questions about SparkER write us at [email protected]
 [4] Papadakis, G., Ioannou, E., Niederée, C., & Fankhauser, P. (2011). Efficient entity resolution for large heterogeneous information spaces. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining - WSDM ’11, 535.
 
 [5] Gagliardelli, L., Zhu, S., Simonini, G., & Bergamaschi, S. (2018). Bigdedup: a Big Data integration toolkit for duplicate detection in industrial scenarios. In 25th International Conference on Transdisciplinary Engineering (TE2018) (Vol. 7, pp. 1015-1023). [link](https://iris.unimore.it/retrieve/handle/11380/1165040/201434/ATDE7-1015.pdf)
+
+[6] Gagliardelli, L., Papadakis, G., Simonini, G., Bergamaschi, S., & Palpanas, T. (2022). Generalized Supervised Meta-Blocking. In PVLDB. [link](https://arxiv.org/abs/2204.08801)