This paper develops a model for learning to answer queries in knowledge bases with incomplete data about relations between entities. For example, the running example in the paper is answering queries like HasOfficeInCountry(Uber,?), when the relation is not directly present in the knowledge base, but supporting relations like HasOfficeInCity(Uber, NYC) and CityInCountry(NYC, USA). The aim in this work is to learn rules like HasOfficeInCountry(A, B) HasOfficeInCountry(A, C) && CityInCountry(C, B). Note that this is a bit different from learning embeddings for entities in a knowledge base, because the rule to be learned is abstract, not depending on any specific entities. The formulation in this paper is cast the problem as one of learning two components: - a set of rules, represented as a sequence of relations (those that appear in the RHS of the rule) - a real-valued confidence on the rule The approach to learning follows ideas from Neural Turing Machines and differentiable program synthesis, whereby the discrete problem is relaxed to a continuous problem by defining a model for executing the rules where all rules are executed at each step and then averaged together with weights given by the confidences.

We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog [5], where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.

We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog [5], where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.

We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog [5], where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.