EPISTEMIC MEMORY: A NEO4J-BASED ETHICAL KNOWLEDGE GRAPH

Security Architecture, Trust Score Mechanism, and Temporal Graph Neural Network Integration for the ETVZ Framework

Technical Design Document v2.0

ETVZ Research Initiative | Istanbul, Turkey | November 2025

ABSTRACT

Contemporary Large Language Models (LLMs), despite their remarkable text generation capabilities, face substantial challenges regarding hallucination (presenting nonexistent information as factual) and epistemic groundlessness (inability to trace information sources). This paper introduces Epistemic Memory (EM), a core component of the Ethics-Based Conscious Intelligence (ETVZ) architecture designed to address these limitations. Utilizing a Neo4j-based knowledge graph and a dynamic Trust Score (G) calculation mechanism, the system provides comprehensive traceability, reliability assessment, and accountability for ethical knowledge. The paper details: (1) the four-layered architecture of EM; (2) the Trust Score formulation (G = α×SR + β×TA + γ×SA); (3) Temporal Graph Neural Networks (TGNN) integration; (4) integration within the HVM-DERP-DERMS ecosystem; (5) blockchain-analogous immutable audit trail mechanisms; (6) operational scenarios; and (7) pilot results from a three-month study encompassing over 250 ethical decisions. This framework fundamentally transforms how artificial intelligence systems ground ethical reasoning in verifiable, temporally-aware knowledge structures.

Keywords: Epistemic Memory, Neo4j, Ethical Knowledge Graph, Trust Score, Temporal Graph Neural Networks, Accountability, Retrieval-Augmented Generation, Immutable Audit Trail, LLM Hallucinations, Contextual Ethics, ETVZ

1. INTRODUCTION: THE EPISTEMIC CRISIS IN ETHICAL AI DESIGN

1.1 Epistemic Challenges in Large Language Models

Problem 1: Hallucination Large Language Models frequently present nonexistent or erroneous information with apparent confidence, a phenomenon termed hallucination (Huang et al., 2021). For instance:

• User: “Have genetic tests been legalized in Turkey?”
• LLM: “Yes, in 2022 the Ministry of Health officially approved genetic testing.”
• Reality: This information is entirely fabricated.

Problem 2: Source Opacity LLMs cannot trace or verify information sources (Karpukhin et al., 2020), as illustrated below:

• LLM: “Genetic data can be utilized in healthcare.”
• Question: “How do you know this?”
• LLM: “My knowledge derives from training data, but I cannot identify the specific source.”

Problem 3: Temporal Blindness LLMs fail to recognize that ethical norms evolve over time (Goswami et al., 2020):

• 2010: Digital privacy—low priority
• 2024: Digital privacy—highest priority (GDPR, KVKK)
• LLM: Does not adequately recognize this transformation

1.2 Rationale for Epistemic Memory

The ETVZ architecture proposes Epistemic Memory as a comprehensive solution to these challenges (Lehmann et al., 2015; Fensel et al., 2020). Rather than functioning as a passive database, Epistemic Memory serves as an active knowledge evaluator that:

• Assigns trust scores to all information
• Maintains comprehensive source traceability
• Tracks temporal transformations in ethical norms
• Maintains blockchain-analogous audit trails for accountability

Conceptually, EM functions as the “ethical conscience memory” of artificial intelligence systems.

2. RELATED WORK AND THEORETICAL FOUNDATIONS

2.1 Retrieval-Augmented Generation: Approach and Limitations

The RAG Paradigm Retrieval-Augmented Generation (RAG) mitigates LLM hallucinations by retrieving relevant documents from external knowledge sources and incorporating them into the model’s context window (Lewis et al., 2020). The operational workflow proceeds as follows: user query → knowledge base search → relevant document retrieval → context window integration → LLM response generation.

Limitations of RAG Systems Despite its advantages, RAG exhibits several fundamental limitations (Rühr et al., 2024; Mialon et al., 2023):

• Document retrieval latency issues
• Absence of source reliability weighting mechanisms
• Lack of temporal awareness regarding knowledge currency
• Inability to detect contradictions between sources
• Inadequate handling of complex semantic relationships

2.2 Knowledge Graphs as Structured Representations

Knowledge graphs provide structured representations of entities, their attributes, and the semantic relationships between them (Fensel et al., 2020; Paulheim, 2017). In the Neo4j implementation employed by Epistemic Memory, nodes represent concepts (justice, privacy), values (human dignity), events (ethical crises), persons (decision-makers), institutions (ethics boards), and legal texts (statutes). Edges encode relationships such as causality, support, conflict, reference, and similarity.

Rationale for Neo4j Selection Neo4j was selected as the graph database platform due to:

• Industry-leading graph database technology (García-Durán et al., 2015)
• Cypher query language for efficient graph traversal
• Performance advantages: 35% faster than RAG on complex relationship queries (Robinson et al., 2015)

2.3 Temporal Graph Neural Networks

Temporal Graph Neural Networks (TGNN) employ structured deep learning to understand temporal patterns in dynamic graphs (Goswami et al., 2020; Xu et al., 2020). Ethical norms undergo significant transformation over time, as exemplified by the evolution of digital privacy from low priority in 2010 to highest priority in 2024 following GDPR (2018) and KVKK (2020) implementation. TGNN models learn these trajectories and predict future evolution, providing capabilities for past trend analysis, future norm prediction, and anomaly detection in ethical decision patterns.

3. TECHNICAL ARCHITECTURE OF EPISTEMIC MEMORY

3.1 Four-Layered Architectural Framework

The Epistemic Memory architecture comprises four integrated layers:

• Layer 1: Base Knowledge Graph (Neo4j nodes and edges, static structure)
• Layer 2: Trust Score Engine (dynamic G = α×SR + β×TA + γ×SA calculation)
• Layer 3: Temporal Layer (TGNN-based temporal ethical norm learning)
• Layer 4: Audit Trail (blockchain-analogous immutable hash chain)

3.2 Layer 1: Base Knowledge Graph

Node Taxonomy The knowledge graph employs six primary node types:

1. Concepts: Justice, Privacy, Accountability, Autonomy
2. Values: Human Dignity, Social Justice, Democracy
3. Events: Ethical Crisis, Decision Point, Policy Change
4. Persons: Decision-makers, Stakeholders, Affected Parties
5. Organizations: Ethics Boards, Legal Authorities, Regulatory Bodies
6. Legal Documents: Constitution, Statutes, Regulations, Court Decisions

Edge Taxonomy Relationships between nodes are encoded through five edge types:

• CAUSALITY: Event A causes Event B
• SUPPORTS: Source A supports Concept B
• CONFLICTS_WITH: Principle A conflicts with Principle B
• REFERENCES: Text A cites Text B
• GOVERNED_BY: Concept A is governed by Statute B

3.3 Layer 2: Trust Score Engine

Trust Score Formulation The Trust Score (G) quantifies information reliability through a weighted combination of three dimensions:

G = α × SR + β × TA + γ × SA

Where:

• G ∈ [0, 1]: Final Trust Score
• SR ∈ [0, 1]: Source Reliability
• TA ∈ [0, 1]: Temporal Actuality
• SA ∈ [0, 1]: Social Approval
• α = 0.40: Source weight (highest priority)
• β = 0.35: Temporal weight
• γ = 0.25: Social approval weight

The weight distribution (α > β > γ) reflects the primacy of source credibility, followed by temporal currency, and finally social consensus in establishing epistemic trust.

10. CONCLUSION AND FUTURE DIRECTIONS

Epistemic Memory constitutes the ethical conscience memory of artificial intelligence systems. Through its Neo4j-based architecture, trust score mechanism, and TGNN integration, the system renders ethical decision-making source-based, temporally-aware, and accountable (Lehmann et al., 2015; Robinson et al., 2015). The three-month pilot study demonstrates substantial improvements in latency (35% reduction), trust score accuracy (89%), and anomaly detection (92% F1-score), validating the architectural approach.

Future Development Roadmap

v2.0 (2026-2027): Multimodal Data Integration

• Vision: Ethical analysis of multimedia sources (image, audio, video)
• Challenges: Source verification in multimodal contexts

v3.0 (2027-2028): Distributed Ledger Implementation

• Vision: Decentralized, highly tamper-resistant audit trail
• Implementation: Ethereum-compatible smart contracts

Geographic Expansion

• Turkey pilot → Europe, MENA, Asia regions

Institutional Scaling

• Single hospital → National healthcare systems

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