Knowledge Graphs (KG) are the backbone of many data-intensive applications since they can represent data coupled with its meaning and context. Aligning KGs across different domains and providers is necessary to afford a fuller and integrated representation. A severe limitation of current KG alignment (KGA) algorithms is that they fail to articulate logical thinking and reasoning with lexical, structural, and semantic data learning. Deep learning models are increasingly popular for KGA inspired by their good performance in other tasks, but they suffer from limitations in explainability, reasoning, and data efficiency. Hybrid neurosymbolic learning models hold the promise of integrating logical and data perspectives to produce high-quality alignments that are explainable and support validation through human-centric approaches. This paper examines the current state of the art in KGA and explores the potential for neurosymbolic integration, highlighting promising research directions for combining these fields.

On the topic of creating art, Spanish surrealist painter Joan Miro once said "the works must be conceived with fire in the soul, but executed with clinical coolness." No matter how much cool compute they may pack, how can today's AI models hope to access that essential "fire in the soul" when generating their artworks? In a new paper, researchers from Adobe, Georgia Tech, and Facebook AI Research propose a neuro-symbolic hybrid approach to address the challenge of creativity in generative art. Generative art refers to the creation of artworks using autonomous processes with no direct human control. There are two general classes of generative art: "neural," where a deep neural network is trained to generate samples from a data distribution; and "symbolic" or "algorithmic," where a human artist designs the primary parameters and an autonomous system then works within these constraints to generate samples.