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Towards nonmonotonic relational learning from knowledge graphs. (English) Zbl 1420.68180

Cussens, James (ed.) et al., Inductive logic programming. 26th international conference, ILP 2016, London, UK, September 4–6, 2016, Revised selected papers. Cham: Springer. Lect. Notes Comput. Sci. 10326, 94-107 (2017).
Summary: 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.
For the entire collection see [Zbl 1369.68018].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68N17 Logic programming
68T30 Knowledge representation
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