  Multi-relational databases are the basis of most consolidated data
collections in science and industry today. Most learning and mining algorithms,
however, require data to be represented in a propositional form. While there is
a variety of specialized machine learning algorithms that can operate directly
on multi-relational data sets, propositionalization algorithms transform
multi-relational databases into propositional data sets, thereby allowing the
application of traditional machine learning and data mining algorithms without
their modification. One prominent propositionalization algorithm is RELAGGS by
Krogel and Wrobel, which transforms the data by nested aggregations. We propose
a new neural network based algorithm in the spirit of RELAGGS that employs
trainable composite aggregate functions instead of the static aggregate
functions used in the original approach. In this way, we can jointly train the
propositionalization with the prediction model, or, alternatively, use the
learned aggegrations as embeddings in other algorithms. We demonstrate the
increased predictive performance by comparing N-RELAGGS with RELAGGS and
multiple other state-of-the-art algorithms.
