Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning

Abstract: Using generative Artificial Intelligence (AI), we transformed a set of 1,000 scientific papers in the area of biological materials into detailed ontological knowledge graphs, revealing their inherently scale-free nature. Using graph traversal path detection between dissimilar concepts based on combinatorial ranking of node similarity and betweenness centrality, we reveal deep insights into unprecedented interdisciplinary relationships that can be used to answer queries, identify gaps in knowledge, and propose never-before-seen material designs and their behaviors. One comparison revealed detailed structural parallels between biological materials and Beethoven's 9th Symphony, highlighting shared patterns of complexity through isomorphic mapping. The algorithm further created an innovative hierarchical mycelium-based composite that incorporates joint synthesis of graph sampling with principles extracted from Kandinsky's Composition VII painting, where the resulting composite reflects a balance of chaos and order, with features like adjustable porosity, mechanical strength, and complex patterned chemical functionalization. We uncover other isomorphisms across physical, biological, and artistic spheres, revealing a nuanced ontology of immanence and material flux that resonates with postmodern philosophy, and positions these interconnections within a heterarchical framework. Our findings reveal the dynamic, context-dependent interplay of entities beyond traditional hierarchical paradigms, emphasizing the significant role of individual components and their fluctuative relationships within the system. Our predictions achieve a far higher degree of novelty, technical detail and explorative capacity than conventional generative AI methods. The approach establishes a widely useful framework for innovation by revealing hidden connections that facilitate discovery.