DOI: 10.7910/dvn/w5ej8h
title: SEGN: A Philosophy-Driven Framework for Condition-Dependent AI Reasoning（Comprehensive system solutions）

description: This project systematically releases the theoretical model, digital product system, and accompanying tools and data resources of the Structured Empty Graph Network (SEGN). It aims to address the current tendency of artificial intelligence systems to generate ‘pseudo-definite outputs’ and illusory inferences under conditions of incomplete information, structural discontinuity, and insufficient constraints.

SEGN proposes an inference paradigm where ‘structure precedes conclusion, and conditions precede generation,’ fundamentally distinguishing itself from output-completeness-oriented generative models. Through formalised conditional dependency graphs, this framework explicitly models causal relationships, contextual cues, and logical premises. When structural closure is unattainable or evidence is insufficient, it triggers an explicit VOID (Validated Out-of-Domain/Void Termination) mechanism. This systemically blocks inferences lacking structural grounding, transforming ‘output refusal’ into an auditable, explainable, and safety-compliant reasoning state.

Structurally, SEGN comprises five core modules: the Conditional Dependency Layer, Graph Attention Inference Core, VOID Response Mechanism, Latent Graph Memory Module, and Counterfactual & Explainable Inference Module. This architecture not only supports robust inference under complete structural conditions but also reactivates latent pathways following conditional evolution or structural repair, enabling a closed-loop ‘refuse-repair-explain’ reasoning process.

This project concurrently releases a deployable digital product system built upon the SEGN methodology, alongside a suite of structured data resources and toolchains publicly available on Harvard Dataverse. The associated datasets systematically employ full-scale, lossless knowledge graph extraction and auditing methods. SEGN's structural validation and VOID assessment serve as quality gateways, preventing unconditional strong generation and untraceable assertions. This ensures reproducibility and structural auditability throughout the data construction process. All resources are published with public DOIs, supporting independent verification, reuse, and long-term citation.

Collectively, this project not only proposes an original reasoning framework for high-uncertainty environments but also demonstrates its engineering into deployable, auditable AI systems and open scientific infrastructure. SEGN provides a systematic solution for constructing next-generation AI systems that are hallucination-resistant, explainable, and possess defined accountability boundaries. It is applicable to demanding scenarios such as governance analysis, risk assessment, decision support, and complex systems modelling.
description: How to use it to ‘extract other graphs conforming to SEGN standards’
A reusable SEGN standard ‘parent-child’ generation paradigm is now established:
Employ the lossless parent graph (Level 7) as the ‘single source of truth’ (where any semantics can be traced back to the original text).
Employ derived semantic graphs (concept/mechanism/event + edges) as the ‘computable reasoning layer’.
Utilise Evidence Alignment as SEGN's ‘audit interface’:
Any concept/mechanism/causal edge → can be traced back to its source evidence node (source_node_id + source_text).
When establishing a SEGN standard graph for new documents, simply reuse the same field structure and alignment rules:
First generate the Level 7 lossless master graph
Then regenerate the derived semantic graph
Finally regenerate the evidence alignment table
Together, these form a transferable, auditable, and scalable SEGN standardised data product.