Contract Knowledge Graphs Overview

• Contract knowledge graphs are formal, machine-interpretable representations of contractual entities, rules, and processes, enabling structured querying and compliance verification.
• They employ modular, ontology-driven construction methodologies using techniques like NLP, ASTs, and CFGs to extract and organize contract data across diverse domains.
• They support advanced querying and reasoning with SPARQL, SHACL, and graph-based methods to facilitate smart contract auditing, legal analysis, and automated code synthesis.

Contract knowledge graphs are formal, machine-interpretable representations of contractual entities, rules, and processes, enabling advanced querying, reasoning, compliance verification, and automation across diverse contract domains. They are increasingly foundational in domains including smart contract analysis, contract compliance verification, construction contract review, legal scenario reasoning, and automated contract code generation, supporting the systematic modeling, validation, and exploitation of both natural language and code-based contract artifacts.

1. Formal Models and Ontological Foundations

Contract knowledge graphs instantiate structured, directed, labeled graphs $G = (V, E, \tau_V, \tau_E)$, where $V$ denotes contract entities, $E$ denotes relations, and $\tau_V$, $\tau_E$ define node and edge types, respectively. Ontology layers encode classes, properties, and domain relationships, with instance layers populating these schemas with concrete contract data (Li et al., 7 Dec 2025). Prominent ontological foundations include:

• FIBO, smashHitCore, ODRL: Used for financial contracts, GDPR compliance, and rights/obligations modeling (David et al., 21 Jul 2025).
• HL7 FHIR OWL2: Adopted for healthcare-related contract KGs to ensure semantic interoperability with established standards (Woensel et al., 11 Sep 2024).
• Domain-specific ontologies: Tailored for construction contracts (NCKG) (Zheng et al., 2023), IRAC-oriented legal KGs (Kang et al., 19 Jun 2024), and business process models (DCR graphs) (Eshghie et al., 2023).

Such schemas enable hierarchical modeling (e.g., Chapters $\rightarrow$ Sections $\rightarrow$ Interpretations), explicit representation of multi-level concepts (main/sub), rule/obligation encoding, and strict type systems suitable for compliance and automation.

2. Construction Methodologies and Pipelines

KG construction methods differ by contract type and target application:

• Code-centric contracts (e.g., Solidity smart contracts): Extraction merges AST, control-flow graph (CFG), and single static assignment (SSA) representations, using tools such as Slither to enumerate nodes for contracts, functions, modifiers, state variables, statement sub-types, and identifiers (Li et al., 7 Dec 2025). Edges represent containment, control/data flow, invocation, inheritance, and execution order.
• Text-centric contracts (construction/legal): Multi-stage NLP-driven pipelines extract behaviors (actor-action-object), statements (property assignments), constraints (temporal/content/purpose), and fact-to-fact logical/temporal links. Passive→active reformulation, constraint pattern matching, and ontology-guided entity grouping are systematically employed (Zheng et al., 2023, Kang et al., 19 Jun 2024).
• Semantic code generation: High-level ontological definitions (OWL2), rule bodies (Notation3), and imperative logic extraction compose a bridge representation, converted into target smart contract languages (e.g., Solidity) with auxiliary event/ADT mappings and constraint checks (Woensel et al., 11 Sep 2024).
• Interactive expert annotation: Expansion of the concept hierarchy and cross-referencing with statutes, interpretations, and case law (for legal KGs) ensures comprehensive coverage of relevant legal concepts (Kang et al., 19 Jun 2024).

Overall, methodologies emphasize reproducibility, modular construction, and close alignment with underlying legal, financial, or software artifacts.

3. Querying, Reasoning, and Verification

Contract KGs facilitate expressive queries and automated reasoning using triple-store technologies (SPARQL, Cypher), shape constraint languages (SHACL), and rule-based validation:

• Vulnerability detection: Parameterized SPARQL templates allow precise discovery of access-control and code-level vulnerabilities in smart contracts by pattern-matching over entity/relation subgraphs (Li et al., 7 Dec 2025).
• Compliance and consistency checking: SHACL Core shapes formalize requirements such as unique contract status, obligation timelines, and violation propagation. Answer Set Programming (ASP) via clingo can compute minimal, user-preferred repairs for detected inconsistencies; these solutions are both auditable and correct-by-construction (David et al., 21 Jul 2025).
• Behavioral/process modeling: DCR graphs extend query/verification capabilities with temporal and role semantics—queries detect pending obligations, unreachable events, or deadline violations in business process contracts (Eshghie et al., 2023).
• Retrieval-augmented reasoning: GraphRAG and embedding-based retrieval combine symbolic subgraph matching with neural encoding for clause-to-knowledge grounding, boosting both accuracy and interpretability in clause review and risk identification (Zheng et al., 2023).
• Legal-reasoning support: Concept-driven Cypher queries support issue/rule retrieval and application tracking within the IRAC framework, tightly aligning model predictions with statutory sources and case law (Kang et al., 19 Jun 2024).

Collectively, these mechanisms enable both static and dynamic analysis, compliance assurance, process monitoring, and advanced semantic search over contract corpora.

4. Integration with Automation, LLMs, and Code Synthesis

Recent research leverages contract KGs as intermediaries for advanced LLM-based automation and direct code synthesis:

• LLM-driven query generation: Frameworks such as CKG-LLM employ two-stage, chain-of-thought prompting, converting natural language vulnerability descriptions directly into executable graph queries (SPARQL), achieving superior accuracy for vulnerability detection in smart contracts (Li et al., 7 Dec 2025).
• Retrieval-augmented generation (GraphRAG): LLMs receive both contract clauses and serialized KG-derived subgraphs, enhancing task decomposition, risk assessment, and auditability. Risk identification and interpretability metrics significantly improve over LLMs alone (Zheng et al., 2023).
• Automated code generation: High-level KGs and declarative rules (OWL2 + N3) drive off-chain pipelines that emit small, gas-efficient smart contract code with built-in type/ADT mapping, on-chain event logic, and oracle integration for external data fetches (Woensel et al., 11 Sep 2024). Explicit economic analysis demonstrates cost-effectiveness and correctness.
• Neuro-symbolic IRAC systems: Legal scenario analyzers interface LLMs with structured SKGs to enforce statutory anchoring, step-by-step application, and reduction of hallucinations, yielding substantial gains in all IRAC pipeline stages (Kang et al., 19 Jun 2024).

Each approach reinforces the value of contract KGs as both reasoning substrates and code/logic scaffolds for scalable, auditable automation.

5. Applications, Evaluation, and Empirical Findings

The utility of contract knowledge graphs has been empirically validated across a spectrum of domains:

• Smart contract auditing: Access-control, integer overflow, reentrancy, and compliance vulnerabilities are automatically detected with higher recall and precision compared to baseline tools (Li et al., 7 Dec 2025).
• GDPR and CCV: Consistency requirements (status uniqueness, obligation deadlines, violation propagation) are validated and automatically repaired at scale, with batch repair runtimes remaining practical even for thousands of violations. The resulting KGs are shown to remain consistent after repair (David et al., 21 Jul 2025).
• Construction contract review: NCKG-enhanced LLMs achieve an F1 of 0.85 in risk identification (vs. 0.78 for vanilla GPT-4) and 92% citation-based interpretability, supporting reliable, explainable contract analysis (Zheng et al., 2023).
• Legal scenario reasoning: Integration of SKGs with IRAC analysis yields increases in issue agreement (+21.4%), rule recall@5 (+60%), application step agreement (+18.9%), and conclusion quality (+71.4%). Findings indicate substantial reduction in hallucination and error explainability (Kang et al., 19 Jun 2024).
• Smart contract synthesis: Automatic code generation from HL7 FHIR-based KGs achieves expected contract outcomes with gas costs comparable to or lower than handcrafted equivalents, and with demonstrably small binary sizes (Woensel et al., 11 Sep 2024).

A plausible implication is that contract KGs will continue to reduce audit cost, error rates, and compliance risks in production contract-management workflows.

6. Architectural Extensions and Future Directions

Multiple architectural, methodological, and application-oriented extensions have been explored:

• Ontology/instance separation: Maintaining clear boundaries enables rapid adaptation to evolving contract languages and standards (Li et al., 7 Dec 2025).
• Semantic interoperability: By construction, KGs based on standard ontologies (FHIR, ODRL) support cross-organizational data exchange, code generation, and multi-chain contract deployment (Woensel et al., 11 Sep 2024, David et al., 21 Jul 2025).
• Repair and explainability: User-guided repair, preference encoding, and optimality rules ensure maintainable and auditable contract knowledge bases (David et al., 21 Jul 2025).
• Scalability and generalizability: Rule-driven and modular pipelines (NLP, GraphRAG, prompt-based IE) facilitate extension to new contract types, languages, and domains (Zheng et al., 2023).
• Process traceability and runtime verification: Integration with DCR graphs and RDF/OWL frameworks supports automated compliance checks, runtime monitoring, and on-chain/off-chain alignment (Eshghie et al., 2023).
• Bidirectional KG–LLM workflows: LLMs both bootstrap KG construction (entity/fact extraction) and leverage KG-derived subgraphs for reliable, grounded reasoning (Zheng et al., 2023, Kang et al., 19 Jun 2024).
• Performance optimization: Off-chain code generation and minimal ADT extraction enable contracts to remain within stringent blockchain gas and size constraints (Woensel et al., 11 Sep 2024).

These developments suggest a trajectory toward fully interoperable, adaptive, and self-explaining contract automation environments grounded in contract knowledge graph infrastructures.