Scenarios Interpretation with Prior Knowledge

Statistical Relational Learning (SRL) deals with relational domains, where the samples are neither independent nor uniformly distributed. Moreover, central to SRL is the integration of logical knowledge in the learning framework. The main tasks in SRL are Collective Classification, Entity Resolution, Link Prediction and Knowledge Graph Completion. In this extended abstract we propose a new supervised learning task called Scenarios Interpretation (SI) where a sample is a Scenario, i.e. a set of (typically few) objects where each object and pair of objects have its own features. The goal is to classify objects and relationships. We propose NIoS (Neural Interpeter of Scenarios), a method for solving SI that is able to inject Prior Knowledge expressed in First Order Logic (FOL) into a neural network model. We implemented a first version and tested it on Visual Relationship Detection task (VRD) showing that NIoS outperformed state of the art systems.