We present a method and its implementation Dist-Μ-RA for the optimized distributed evaluation of recursive relational algebraic terms. This method provides a systematic parallelisation technique by means of fixpoint splitting plan generation and selection. The goal is to offer expressivity for high-level queries while providing efficiency and reducing communication costs. Experimental results on both real and synthetic graphs show the effectiveness of the proposed approach compared to existing systems

We demonstrate a system for recursive query answering over graphs. The system is based on a complete implementation of the recursive relational algebra µ-RA, extended with parsers and compilers adapted for queries over knowledge and property graphs. Each component of the system comes with novelty for processing recursion. As a result, one can formulate, optimize and efficiently answer expressive queries that navigate recursively along paths in different types of graphs. We demonstrate the system on real datasets and show how it performs considering other state-of-the-art systems.

Graph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web. However, while being rich in information, graphs are often noisy and incomplete. As a result, graph completion tasks, such as node classification or link prediction, have gained attention. On one hand, neural methods, such as graph neural networks, have proven to be robust tools for learning rich representations of noisy graphs. On the other hand, symbolic methods enable exact reasoning on graphs. We propose Knowledge Enhanced Graph Neural Networks (KeGNN), a neuro-symbolic framework for graph completion that combines both paradigms as it allows for the integration of prior knowledge into a graph neural network model. Essentially, KeGNN consists of a graph neural network as a base upon which knowledge enhancement layers are stacked with the goal of refining predictions with respect to prior knowledge. We instantiate KeGNN in conjunction with two well-known graph neural networks, Graph Convolutional Networks and Graph Attention Networks, and evaluate KeGNN on multiple benchmark datasets for node classification.