scholarly journals Transforming Graph Data for Statistical Relational Learning

2012 ◽  
Vol 45 ◽  
pp. 363-441 ◽  
Author(s):  
R. A. Rossi ◽  
L. K. McDowell ◽  
D. W. Aha ◽  
J. Neville
Keyword(s):  
Relational Learning ◽  
Statistical Relational Learning ◽  
Data Representation ◽  
Information Networks ◽  
Biological Information ◽  
Relational Data ◽  
Symmetric Representation ◽  
Graph Data ◽  
Data Representations ◽  
Relational Domains

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this article, we examine and categorize techniques for transforming graph-based relational data to improve SRL algorithms. In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms. We introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. More specifically, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) system- atically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey competing approaches for each of these tasks. We also dis- cuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.

scholarly journals Induction of Interpretable Possibilistic Logic Theories from Relational Data

2017 ◽  
Author(s):  
Ondrej Kuzelka ◽  
Jesse Davis ◽  
Steven Schockaert
Keyword(s):  
Neural Networks ◽  
Probabilistic Models ◽  
Relational Learning ◽  
Statistical Relational Learning ◽  
Relational Data ◽  
Markov Logic Networks ◽  
Relational Models ◽  
Markov Logic ◽  
Possibilistic Logic ◽  
Interpretable Models

The field of statistical relational learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which makes them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly encode what can be derived with a given level of certainty. Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.

Software Vulnerabilities, Products and Exploits: A Statistical Relational Learning Approach

2021 ◽  
Author(s):  
Caina Figueiredo ◽  
Joao Gabriel Lopes ◽  
Rodrigo Azevedo ◽  
Gerson Zaverucha ◽  
Daniel Sadoc Menasche ◽  
...  
Keyword(s):  
Relational Learning ◽  
Statistical Relational Learning ◽  
Learning Approach ◽  
Software Vulnerabilities

scholarly journals Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation

2017 ◽  
Author(s):  
Sebastijan Dumancic ◽  
Hendrik Blockeel
Keyword(s):  
Relational Learning ◽  
Representation Learning ◽  
Relational Data ◽  
Distributed Representation ◽  
Learning Tasks ◽  
Wide Range ◽  
Lower Complexity ◽  
Classification Tasks ◽  
Latent Representations

The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier. Existing methods typically focus on vectorized data and offer little support for relational data, which additionally describes relationships among instances. In this work we introduce an approach for relational unsupervised representation learning. Viewing a relational dataset as a hypergraph, new features are obtained by clustering vertices and hyperedges. To find a representation suited for many relational learning tasks, a wide range of similarities between relational objects is considered, e.g. feature and structural similarities. We experimentally evaluate the proposed approach and show that models learned on such latent representations perform better, have lower complexity, and outperform the existing approaches on classification tasks.

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