Transfer Learning with Active Queries for Relational Data Modeling Across Multiple Information Networks

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-04182-3_20 ◽

2018 ◽

pp. 220-229

Author(s):

Ke-Jia Chen ◽

Kai Zhang ◽

Xi-Lin Jiang ◽

Yunyun Wang

Keyword(s):

Transfer Learning ◽

Data Modeling ◽

Information Networks ◽

Relational Data

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An NML-based model selection criterion for general relational data modeling

2013 IEEE International Conference on Big Data ◽

10.1109/bigdata.2013.6691603 ◽

2013 ◽

Cited By ~ 3

Author(s):

Yoshiki Sakai ◽

Kenji Yamanishi

Keyword(s):

Model Selection ◽

Selection Criterion ◽

Data Modeling ◽

Relational Data ◽

Model Selection Criterion

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Transforming Graph Data for Statistical Relational Learning

Journal of Artificial Intelligence Research ◽

10.1613/jair.3659 ◽

2012 ◽

Vol 45 ◽

pp. 363-441 ◽

Cited By ~ 19

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.

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