HyDRA: Verifiable Knowledge Graph Construction with Hybrid Reasoning

Introduction

Knowledge graphs are essential for bringing structure, context, and symbolic reasoning into AI systems, but constructing them automatically remains difficult. Generated graphs can be unreliable, inconsistent, or structurally flawed, producing isolated islands of data or confusing abstract classes with concrete instances. This whitepaper presents HyDRA, a hybrid-driven reasoning architecture designed to make knowledge graph automation more verifiable and operationally useful.

The Scope

HyDRA is intended for teams working with domain documents, enterprise knowledge, research corpora, or AI systems that require structured, inspectable knowledge. The paper is most relevant for organizations that want to combine symbolic knowledge with neural generation while keeping graph quality, consistency, and functional correctness under control.

The Process

Given a domain or an initial set of documents, HyDRA first constructs an ontology through a panel of collaborative neurosymbolic agents. These agents agree on competency questions that define what the ontology must be able to answer. The resulting ontology graph then guides triplet extraction from arbitrary documents. Inspired by Design by Contract, the architecture uses verifiable contracts as the main control mechanism for steering LLM generation and checking whether outputs meet the required structure and semantics.

The Results

The outcome is a more reliable path toward automated knowledge graph construction. HyDRA contributes a practical architecture for reducing structural inconsistencies, improving verifiability, and evaluating knowledge graphs by their functional correctness rather than only by static benchmark scores. The work also connects directly to SymbolicAI, using symbolic verification to assess whether the resulting graph can support the reasoning tasks it was built for.