Learning directed relational models with recursive dependencies

Learning directed probabilistic logical models from relational data

Data that has a complex relational structure and in which observations are noisy or partially missing poses several challenges to traditional machine learning algorithms. One solution to this problem is the use of socalled probabilistic logical models (models that combine elements of first-order logic with probabilities) and corresponding learning algorithms. In this thesis we focus on directed...

TRM - Learning Dependencies between Text and Structure with Topical Relational Models

Text-rich structured data become more and more ubiquitous on the Web and on the enterprise databases by encoding heterogeneous structural information between entities such as people, locations, or organizations and the associated textual information. For analyzing this type of data, existing topic modeling approaches, which are highly tailored toward document collections, require manually-defin...

Learning Probabilistic Relational Models

A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with “flat” data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much of the relational structure present in our database. This paper builds on the recent work on probabilistic relationa...

Functional Dependencies Analyse in Fuzzy Relational Database Models

This paper presents a literature overview of Fuzzy Relational Database Models with emphasis on the role of functional dependencies in logical designing and modeling. The aim is the analysis of recent results in this fi eld. Fuzzy set theory is widely applied for the classical relational database extensions resulting in numerous contributions. This is because fuzzy sets and fuzzy logic are power...

Dependencies in Relational Databases