This paper studies semi-supervised object classification in relational data, which is a
fundamental problem in relational data modeling. The problem has been extensively studied …

Self-supervision as an emerging technique has been employed to train convolutional neural
networks (CNNs) for more transferrable, generalizable, and robust representation learning …

Abstract In the Semantic Web vision of the World Wide Web, content will not only be
accessible to humans but will also be available in machine interpretable form as ontological …

This paper demonstrates the use of graphs as a mathematical tool for expressing
independencies, and as a formal language for communicating and processing causal …

A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …

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 …

In many supervised learning tasks, the entities to be labeled are related to each other in
complex ways and their labels are not independent. For example, in hypertext classification …

Many real-world domains are relational in nature, consisting of a set of objects related to
each other in complex ways. This paper focuses on predicting the existence and the type of …

There have been many proposals for first-order belief networks (ie, where we quantify over
individuals) but these typically only let us reason about the individuals that we know about …

This paper1 is about classifying entities that are interlinked with entities for which the class is
known. After surveying prior work, we present NetKit, a modular toolkit for classification in …