In Hybrid AI, machine learning and knowledge engineering are combined to have the best of both worlds. Insights obtained from data are combined with complementary expert knowledge, which can be represented in a graph structure. Graph networks are a recently new development in machine learning and cover methods that learn on graph structured data. This paper researches how knowledge can be incorporated in graph networks for the use case of scene classification. The aim is to detect novel scenes, of which only a few examples and noisy object detections are available. The results show that both using a graph network and adding knowledge can improve performance, however, this is not always necessarily the case. The novelty of this paper is threefold: 1. Using GNNs for scene classification; 2. Combining data and knowledge in GNNs by constructing one input graph; 3. Using GNNs in cases with few training samples and noisy inputs.
We describe a method “Kg2Causal” for using a large-scale, general-purpose biomedical knowledge graph as a prior for data-driven causal network structure learning. Given a set of observed nodes in a dataset, and some relationship edges between the nodes derived from a knowledge graph, Kg2Causal uses the knowledge graph-derived edges to guide the data-driven inference of a causal Bayesian network. We tested Kg2Causal on several real-world biological datasets with known ground-truth networks and demonstrate improvement in network learning accuracy, relative to a baseline of an uninformative network structure prior. We also demonstrate the application of our method if data are collected under different experimental conditions including interventions on the observed variables. The ground-truth network
Deep learning has achieved state-of-the-art results in various application domains ranging from image recognition to language translation and game playing. However, it is now generally accepted that deep learning alone has not been able to satisfy the requirement of fairness and, ultimately, trust in Artificial Intelligence (AI). In this paper, we propose an interactive neural-symbolic approach for fairness in AI based on the Logic Tensor Network (LTN) framework. We show that the extraction of symbolic knowledge from LTN-based deep networks combined with fairness constraints offer a general method for instilling fairness into deep networks via continual learning. Explainable AI approaches which otherwise could identify but not fix fairness issues are shown to be enriched with an ability to improve fairness results. Experimental results on three real-world data sets used to predict income, credit risk and recidivism in financial applications show that our approach can satisfy fairness metrics while maintaining state-of-the-art classification performance.
We report on experiences in adding ML-trained visual recognition modules to a human-oriented image semantic annotation tool which creates RDF descriptions of images and scene contents. We conclude that ML cannot replace expert humans but can aid them in various ways, some unexpected. Semantic markup systems can be to designed to align human and machine blind spots. Finally, we briefly outline directions for future work.
Integration of symbolic and sub-symbolic approaches is rapidly emerging as an Artificial Intelligence (AI) paradigm. This paper presents a proof-of-concept approach towards an unsupervised learning method, based on Restricted Boltzmann Machines (RBMs), for extracting semantic associations among prominent entities within data. Validation of the approach is performed in two datasets that connect language and vision, namely Visual Genome and GQA. A methodology to formally structure the extracted knowledge for subsequent use through reasoning engines is also offered.
