Structure identification in relational data

Structure Learning of Probabilistic Relational Models from Incomplete Relational Data

Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational model (PRM) from incomplete relational data. The missing values are filled in randomly at first, and a maximum likelihood tree (MLT) is generated from the complete data sample. Then, Gibb...

Structure Selection from Streaming Relational Data

Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where relational features are manually defined by a human engineer, parameters are learned for those features on the training data, the resulting mo...

Structure Learning with Hidden Data in Relational Domains

Recent years have seen a surge of interest in learning the structure of Statistical Relational Learning (SRL) models, which combine logic with probabilities. Most of these models apply the closed-world assumption i.e., whatever is not observed is false in the world. We consider the problem of learning the structure of SRL models in the presence of hidden data, i.e. we open the closedworld assum...

Structure Identification in Panel Data Analysis

Metadata Enrichment for Automatic Data Entry Based on Relational Data Models

The idea of automatic generation of data entry forms based on data relational models is a common and known idea that has been discussed day by day more than before according to the popularity of agile methods in software development accompanying development of programming tools. One of the requirements of the automation methods, whether in commercial products or the relevant research projects, ...