Automatic Section Control (ASC) is a long-standing trend for spraying in agriculture. It promises to minimise spray overlap areas. The core idea is to (i) switch off spray nozzles on areas that have already been sprayed, and (ii) to dynamically adjust nozzle flow rates along the boom bar that holds the spray nozzles when velocities of boom sections vary during turn maneuvers. ASC is not possible without sensors for accurate positioning data. Spraying and the movement of modern wide boom bars are highly dynamic processes. In addition, many uncertainty factors have an effect such as cross wind drift, nozzle clogging in open-field conditions, etc. In view of this complexity, the natural question arises if a simpler alternative exist. Therefore, ASC is compared to a proposed simpler one- or two-sections alternative that uses predictive spray switching. The comparison is provided under nominal conditions. Agricultural spraying is intrinsically linked to area coverage path planning and spray switching logic. Combinations of two area coverage path planning and switching logics as well as 3 sections-setups are compared. The three sections-setups differ by controlling 48 sections, 2 sections or controlling all nozzles uniformly with the same control signal as one single section. Methods are evaluated on 10 diverse real-world field examples, including non-convex field contours, freeform mainfield lanes and multiple obstacle areas. An economic cost analysis is provided to compare the methods. A preferred method is suggested that (i) minimises area coverage pathlength, (ii) offers intermediate overlap, (iii) is suitable for manual driving by following a pre-planned predictive spray switching logic for an area coverage path plan, and (iv) and in contrast to ASC can be implemented sensor-free and at low cost. Surprisingly strong economic arguments are found to not recommend ASC for small farms.

This paper presents within an arable farming context a predictive logic for the on- and off-switching of a set of nozzles attached to a boom aligned along a working width and carried by a machinery with the purpose of applying spray along the working width while the machinery is traveling along a specific path planning pattern. Concatenation of multiple of those path patterns and corresponding concatenation of proposed switching logics enables nominal lossless spray application for area coverage tasks. Proposed predictive switching logic is compared to the common and state-of-the-art reactive switching logic for Boustrophedon-based path planning for area coverage. The trade-off between reduction in pathlength and increase in the number of required on- and off-switchings for proposed method is discussed.

How can we mine frequent path regularities from a graph with edge labels and vertex attributes? The task of association rule mining successfully discovers regular patterns in item sets and substructures. Still, to our best knowledge, this concept has not yet been extended to path patterns in large property graphs. In this paper, we introduce the problem of path association rule mining (PARM). Applied to any \emph{reachability path} between two vertices within a large graph, PARM discovers regular ways in which path patterns, identified by vertex attributes and edge labels, co-occur with each other. We develop an efficient and scalable algorithm PIONEER that exploits an anti-monotonicity property to effectively prune the search space. Further, we devise approximation techniques and employ parallelization to achieve scalable path association rule mining. Our experimental study using real-world graph data verifies the significance of path association rules and the efficiency of our solutions.

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners and courses. While these enriched representations allow more accurate and personalized recommendations, explainability remains a significant challenge which is especially problematic for certain domains with significant impact such as education and online learning. Recently, a novel class of recommender systems that uses reinforcement learning and graph reasoning over KGs has been proposed to generate explainable recommendations in the form of paths over a KG. Despite their accuracy and interpretability on e-commerce datasets, these approaches have scarcely been applied to the educational domain and their use in practice has not been studied. In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning. To validate the practical implications of our approach, we conducted a user study examining user perceptions of our new explainable recommendations. We demonstrate the generalizability of our approach by conducting experiments on two educational datasets: COCO and Xuetang.

Adverse Drug Reactions (ADRs) are characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. We propose to mine knowledge graphs for identifying biomolecular features that may enable reproducing automatically expert classifications that distinguish drug causative or not for a given type of ADR. In an explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, we mine a knowledge graph for features; we train classifiers at distinguishing, drugs associated or not with ADRs; we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and we manually evaluate how they may be explanatory. Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR. Experts fully agreed that 73% and 38% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 90% and 77% of them. Knowledge graphs provide diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further.

Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking. Here, we consider a given set of vertices, called seed vertices, and focus on mining their associated neighboring vertices, paths, and, more generally, path patterns that involve classes of ontologies linked with knowledge graphs. Due to the combinatorial nature and the increasing size of real-world knowledge graphs, the task of mining these patterns immediately entails scalability issues. In this paper, we address these issues by proposing a pattern mining approach that relies on a set of constraints (e.g., support or degree thresholds) and the monotonicity property. As our motivation comes from the mining of real-world knowledge graphs, we illustrate our approach with PGxLOD, a biomedical knowledge graph.

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in particular, may suffer from both explanation bias and performance disparity. In this paper, we analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users, and that their recommendations may be biased by the training records of more active users, due to the nature of collaborative filtering, which leads to an unfair treatment by the system. We propose a fairness constrained approach via heuristic re-ranking to mitigate this unfairness problem in the context of explainable recommendation over knowledge graphs. We experiment on several real-world datasets with state-of-the-art knowledge graph-based explainable recommendation algorithms. The promising results show that our algorithm is not only able to provide high-quality explainable recommendations, but also reduces the recommendation unfairness in several respects.

Graph-structured data arise ubiquitously in many application domains. A fundamental problem is to quantify their similarities. Graph kernels are often used for this purpose, which decompose graphs into substructures and compare these substructures. However, most of the existing graph kernels do not have the property of scale-adaptivity, i.e., they cannot compare graphs at multiple levels of granularities. Many real-world graphs such as molecules exhibit structure at varying levels of granularities. To tackle this problem, we propose a new graph kernel called Tree++ in this paper. At the heart of Tree++ is a graph kernel called the path-pattern graph kernel. The path-pattern graph kernel first builds a truncated BFS tree rooted at each vertex and then uses paths from the root to every vertex in the truncated BFS tree as features to represent graphs. The path-pattern graph kernel can only capture graph similarity at fine granularities. In order to capture graph similarity at coarse granularities, we incorporate a new concept called super path into it. The super path contains truncated BFS trees rooted at the vertices in a path. Our evaluation on a variety of real-world graphs demonstrates that Tree++ achieves the best classification accuracy compared with previous graph kernels.

The knowledge base completion problem is the problem of inferring missing information from existing facts in knowledge bases. Path-ranking based methods use sequences of relations as general patterns of paths for prediction. However, these patterns usually lack accuracy because they are generic and can often apply to widely varying scenarios. We leverage type hierarchies of entities to create a new class of path patterns that are both discriminative and generalizable. Then we propose an attention-based RNN model, which can be trained end-to-end, to discover the new path patterns most suitable for the data. Experiments conducted on two benchmark knowledge base completion datasets demonstrate that the proposed model outperforms existing methods by a statistically significant margin. Our quantitative analysis of the path patterns shows that they balance between generalization and discrimination.