Towards Nonmonotonic Relational Learning from Knowledge Graphs
Recent advances in information extraction have led to the so called knowledge graphs (KGs) i.e. huge collections of relational factual knowledge. Since KGs are automatically constructed, they are inherently incomplete, thus naturally treated under the Open World Assumption (OWA) Rule mining techniques have been exploited to support the crucial task of KG completion. However, these techniques can mine Horn rules, which are insufficiently expressive to capture exceptions, and might thus make incorrect predictions on missing links. Recently, a rule based method for filling in this gap was proposed which, however, applies to a flattened representation of a KG with only unary facts. In this work we make the first steps towards extending this approach to KGs in their original relational form, and provide preliminary evaluation results on real world KGs, which demonstrate the effectiveness of our method.
ILP