Wordification: Propositionalization by unfolding relational data into bags of words

Perovšek, Matic; Vavpetič, Anže; Kranjc, Janez; Cestnik, Bojan; Lavrač, Nada

doi:10.1016/j.eswa.2015.04.017

Cited by 21 publications

(25 citation statements)

References 26 publications

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“…( 2018 ), we implemented a variant of this approach, capable of learning directly from large, sparse matrices that are returned by the Wordification (Perovšek et al. 2015 ) approach to propositionalization of relational databases. In this work, following the paradigm of propositionalization by Wordification, each instance is described by a bag (a multiset that allows for multiple appearances of its elements) of features of the form TableName AttributeName Value .…”

Section: Proposed Unification Methodology and Its Two Implementationsmentioning

confidence: 99%

“…This section presents the implementations of the proposed methods, preceded by the description of the updates to the Wordification algorithm (Perovšek et al. 2015 for multi- propositionalization algorithm presented in Sect. 6.2.1 .…”

Section: Proposed Unification Methodology and Its Two Implementationsmentioning

confidence: 99%

“…In this work we significantly extend the ideas proposed in Wordification (Perovšek et al. 2013 , 2015 ) with the aim to maintain the classification performance, yet improve its scalability . Both proposed algorithms build on the idea of Wordification, yet its use in our algorithms is differentiated by the following design decisions: Inputs do not need to be hosted in relational databases.…”

Section: Proposed Unification Methodology and Its Two Implementationsmentioning

confidence: 99%

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Propositionalization and embeddings: two sides of the same coin

2020

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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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Section: Proposed Unification Methodology and Its Two Implementationsmentioning

confidence: 99%

Section: Proposed Unification Methodology and Its Two Implementationsmentioning

confidence: 99%

Section: Proposed Unification Methodology and Its Two Implementationsmentioning

confidence: 99%

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Propositionalization and embeddings: two sides of the same coin

2020

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“…Other ILP algorithms often deal with numeric attributes as if they were categorical. For example, recent propositionalization algorithms such as RSD (Zelezný & Lavrac, 2006), HiFi (Kuželka & Železný , 2008), RelF (Kuzelka & Zelezný , 2009), LBP (Dinh, Vrain, & Exbrayat, 2012), BCP (Ontañón & Plaza, 2015) and Wordification (Perovšek, Vavpetič, Kranjc, Cestnik, & Lavrač, 2015), most of them available on the ClowdFlows platform (Lavrač, Perovšek, & Vavpetič, 2014), do not deal explicitly with continuous attributes.…”

Section: Discretizationmentioning

confidence: 98%

Flexible propositionalization of continuous attributes in relational data mining

Ahmed

Lachiche

Charnay

et al. 2015

Expert Systems with Applications

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“…It can be considered as a propositionalization method, with one feature per value in a secondary table. These features are counted as words in a document in the wordification approach (Perovsek et al 2015). In 1BC they are used as features in a propositional naive Bayesian classifier.…”

Section: Competing Algorithmsmentioning

confidence: 99%

A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data

2018

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Companies want to extract value from their relational databases. This is the aim of relational data mining. Propositionalization is one possible approach to relational data mining. Propositionalization adds new attributes, called features, to the main table, leading to an attribute-value representation, a single table, on which a propositional learner can be applied. However, current relational databases are large and composed of mixed, numerical and categorical, data. Moreover, the specificity of relational data is to involve one-to-many relationships. As an example of such data, consider customers purchasing products: each customer can purchase several products. Therefore, there is a need for techniques able to learn complex aggregates. Learning such features means to explore a combinatorial, possibly infinite, space and such an approach is prone to overfitting. We introduce a propositionalization approach dedicated to a robust Bayesian classifier. It efficiently samples a given number of features in the language bias, following a distribution over the complex aggregates. This distribution is also used to penalize complex aggregates in the regularization of the robust Bayesian classifier. Experiments show that it performs better than state-of-the-art methods on most investigated benchmarks and can deal with large datasets more easily. A new real, large, mixed relational dataset is introduced which confirms the ability of our approach to learn complex aggregates.

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Wordification: Propositionalization by unfolding relational data into bags of words

Cited by 21 publications

References 26 publications

Propositionalization and embeddings: two sides of the same coin

Propositionalization and embeddings: two sides of the same coin

Flexible propositionalization of continuous attributes in relational data mining

A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data

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