Semantic Image Interpretation (SII) is the task of extracting structured semantic descriptions from images. It is widely agreed that the combined use of visual data and background knowledge is of great importance for SII. Recently, Statistical Relational Learning (SRL) approaches have been developed for reasoning under uncertainty and learning in the presence of data and rich knowledge. Logic Tensor Networks (LTNs) are a SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and (ii) reasoning with logical formulas describing general properties of the data. In this paper, we develop and apply LTNs to two of the main tasks of SII, namely, the classification of an image's bounding boxes and the detection of the relevant part-of relations between objects. To the best of our knowledge, this is the first successful application of SRL to such SII tasks. The proposed approach is evaluated on a standard image processing benchmark. Experiments show that background knowledge in the form of logical constraints can improve the performance of purely data-driven approaches, including the state-of-theart Fast Region-based Convolutional Neural Networks (Fast R-CNN). Moreover, we show that the use of logical background knowledge adds robustness to the learning system when errors are present in the labels of the training data.

The clinical assessment of mental disorders can be a time-consuming and error-prone procedure, consisting of a sequence of diagnostic hypothesis formulation and testing aimed at restricting the set of plausible diagnoses for the patient. In this article, we propose a novel computerized system for the adaptive testing of psychological disorders. The proposed system combines a mathematical representation of psychological disorders, known as the "formal psychological assessment," with an algorithm designed for the adaptive assessment of an individual's knowledge. The assessment algorithm is extended and adapted to the new application domain. Testing the system on a real sample of 4,324 healthy individuals, screened for obsessive-compulsive disorder, we demonstrate the system's ability to support clinical testing, both by identifying the correct critical areas for each individual and by reducing the number of posed questions with respect to a standard written questionnaire.

Semantic Image Interpretation is the task of extracting a structured semantic description from images. This requires the detection of visual relationships: triples subject, relation, object describing a semantic relation between a subject and an object. A pure supervised approach to visual relationship detection requires a complete and balanced training set for all the possible combinations of subject, relation, object . However, such training sets are not available and would require a prohibitive human effort. This implies the ability of predicting triples which do not appear in the training set. This problem is called zero-shot learning. State-of-the-art approaches to zero-shot learning exploit similarities among relationships in the training set or external linguistic knowledge. In this paper, we perform zero-shot learning by using Logic Tensor Networks, a novel Statistical Relational Learning framework that exploits both the similarities with other seen relationships and background knowledge, expressed with logical constraints between subjects, relations and objects. The experiments on the Visual Relationship Dataset show that the use of logical constraints outperforms the current methods. This implies that background knowledge can be used to alleviate the incompleteness of training sets.

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Measurement is a crucial issue in psychological assessment. In this paper a contribution to this task is provided by means of the implementation of an adaptive algorithm for the assessment of depression. More specifically, the Adaptive Testing System for Psychological Disorders (ATS-PD) version of the Qualitative-Quantitative Evaluation of Depressive Symptomatology questionnaire (QuEDS) is introduced. Such implementation refers to the theoretical background of Formal Psychological Assessment (FPA) with respect to both its deterministic and probabilistic issues. Three models (one for each sub-scale of the QuEDS) are fitted on a sample of 383 individuals. The obtained estimates are then used to calibrate the adaptive procedure whose performance is tested in terms of both efficiency and accuracy by means of a simulation study. Results indicate that the ATS-PD version of the QuEDS allows for both obtaining an accurate description of the patient in terms of symptomatology, and reducing the number of items asked by 40%. Further developments of the adaptive procedure are then discussed.

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