Scientific literature with rich metadata can be represented as a labeled directed graph. This
graph representation enables a number of scientific tasks such as ad hoc retrieval or named …

Humans are often extraordinary at performing practical reasoning. There are cases where
the human computer, slow as it is, is faster than any artificial intelligence system. Are we …

We propose a method to combine the interpretability and expressive power of first-order
logic with the effectiveness of neural network learning. In particular, we introduce a lifted …

This book provides a unique treatment of an important area of machine learning and
answers the question of how kernel methods can be applied to structured data. Kernel …

Markov logic networks (MLNs) are an expressive representation for statistical relational
learning that generalizes both first-order logic and graphical models. Existing methods for …

This Perspective outlines recent progress and future directions for using machine learning
(ML), a data-driven method, to address critical questions in the design, synthesis …

We propose a general framework to incorporate first-order logic (FOL) clauses, that are
thought of as an abstract and partial representation of the environment, into kernel machines …

Constrained pattern mining extracts patterns based on their individual merit. Usually this
results in far more patterns than a human expert or a machine learning technique could …

FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed
(numerical and categorical) data. It generates an (explainable) answer set programming …

In this paper we explore some of the opportunities and challenges for machine learning on
the Semantic Web. The Semantic Web provides standardized formats for the representation …