Propositionalization and embeddings: two sides of the same coin

Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used...

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Bibliographic Details
Published in:Machine learning Vol. 109; no. 7; pp. 1465 - 1507
Main Authors: Lavrač, Nada, Škrlj, Blaž, Robnik-Šikonja, Marko
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2020
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Cognitive tasks
Computer Science
Control
Data integration
Data processing
Format
Machine Learning
Mechatronics
Natural Language Processing (NLP)
Preprocessing
Robotics
Simulation and Modeling
Special Issue of the Inductive Logic Programming (ILP) 2019
Tables (data)
Task complexity
Transformations
Relational learning
Embeddings
Propositionalization
Inductive logic programming
Knowledge graphs
ISSN:0885-6125, 1573-0565
Online Access:Get full text
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Summary:Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches. While both approaches aim at transforming data into tabular data format, they use different terminology and task definitions, are perceived to address different goals, and are used in different contexts. This paper contributes a unifying framework that allows for improved understanding of these two data transformation techniques by presenting their unified definitions, and by explaining the similarities and differences between the two approaches as variants of a unified complex data transformation task. In addition to the unifying framework, the novelty of this paper is a unifying methodology combining propositionalization and embeddings, which benefits from the advantages of both in solving complex data transformation and learning tasks. We present two efficient implementations of the unifying methodology: an instance-based PropDRM approach, and a feature-based PropStar approach to data transformation and learning, together with their empirical evaluation on several relational problems. The results show that the new algorithms can outperform existing relational learners and can solve much larger problems.
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Editors: Dimitar Kazakov and Filip Železny.
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-020-05890-8