Relational learning can be described as the task of learning first-order logic rules from
examples. It has enabled a number of new machine learning applications, eg graph mining …

The employment of Machine Learning (ML) techniques in embedded systems has seen
constant growth in recent years, especially for black-box ML techniques (such as Artificial …

We present High-Throughput Hypothesis Evaluation in Description Logic (HT-HEDL). HT-
HEDL is a high-performance hypothesis evaluation engine that accelerates hypothesis …

We introduce the higher-order refactoring problem, where the goal is to compress a logic
program by discovering higher-order abstractions, such as map, filter, and fold. We …

This article presents MP-SPILDL, a massively parallel inductive logic learner in Description
Logic (DL). MP-SPILDL is a scalable inductive Logic Programming (ILP) algorithm that …

A widely persisting interpretation of Occam's razor is that given two classifiers with the same
training error, the simpler classifier is more likely to generalize better. Within a long-lasting …

Abstract Theory Revision from Examples is the process of repairing incorrect theories and/or
improving incomplete theories from a set of examples. This process usually results in more …

Most search techniques within ILP require the evaluation of a large number of inconsistent
clauses. However, acceptable clauses typically need to be consistent, and are only found at …

Identifying the correct sense of a word in context is crucial for many tasks in natural
language processing (machine translation is an example). State-of-the art methods for Word …

The effective integration of learning and reasoning is a well-known and challenging area of
research within artificial intelligence. Neural-symbolic systems seek to integrate learning …