Higher-Order Knowledge Representations for Agentic Scientific Reasoning

2026-01-08

arXiv.org

AI Summary

Traditional knowledge graphs struggle to capture high-order interactions among multiple entities in scientific reasoning, limiting intelligent inference in complex research scenarios. This work proposes the first hypergraph-based framework for scientific knowledge representation, constructing a global hypergraph comprising 161,172 nodes and 320,201 hyperedges by extracting multi-entity co-occurrence relations from over 1,100 publications on biomaterial composite scaffolds. By leveraging hypergraphs, the approach avoids the combinatorial explosion inherent in pairwise expansions and preserves the contextual integrity of scientific statements. Furthermore, it enables verifiable, teacher-free cross-concept reasoning through hypergraph traversal constrained by node intersections. Experimental results demonstrate the generation of novel, mechanistically interpretable material design hypotheses, such as cerium oxide–chitosan–PCL scaffolds.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

Abstract

Scientific inquiry requires systems-level reasoning that integrates heterogeneous experimental data, cross-domain knowledge, and mechanistic evidence into coherent explanations. While Large Language Models (LLMs) offer inferential capabilities, they often depend on retrieval-augmented contexts that lack structural depth. Traditional Knowledge Graphs (KGs) attempt to bridge this gap, yet their pairwise constraints fail to capture the irreducible higher-order interactions that govern emergent physical behavior. To address this, we introduce a methodology for constructing hypergraph-based knowledge representations that faithfully encode multi-entity relationships. Applied to a corpus of ~1,100 manuscripts on biocomposite scaffolds, our framework constructs a global hypergraph of 161,172 nodes and 320,201 hyperedges, revealing a scale-free topology (power law exponent ~1.23) organized around highly connected conceptual hubs. This representation prevents the combinatorial explosion typical of pairwise expansions and explicitly preserves the co-occurrence context of scientific formulations. We further demonstrate that equipping agentic systems with hypergraph traversal tools, specifically using node-intersection constraints, enables them to bridge semantically distant concepts. By exploiting these higher-order pathways, the system successfully generates grounded mechanistic hypotheses for novel composite materials, such as linking cerium oxide to PCL scaffolds via chitosan intermediates. This work establishes a"teacherless"agentic reasoning system where hypergraph topology acts as a verifiable guardrail, accelerating scientific discovery by uncovering relationships obscured by traditional graph methods.

Problem

• higher-order interactions
• knowledge representation
• scientific reasoning
• hypergraph
• mechanistic hypothesis

Innovation

• hypergraph
• higher-order knowledge representation
• agentic reasoning
• scale-free topology
• mechanistic hypothesis generation