sema-protocol 0.1.0

Semantic Meaning & Agreement Protocol. The missing layer for global AI coordination. Because 'fraud' in Amsterdam ≠ 'fraud' in Johannesburg.

SEMA Protocol

Semantic Meaning & Agreement Protocol

The missing layer for global AI coordination.

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The Problem

'Fraud' in Amsterdam ≠ 'Fraud' in Johannesburg

When AI systems communicate across borders, they face a fundamental problem: the same word means different things in different contexts.

• Legal definitions vary by jurisdiction
• Cultural context affects interpretation
• Regulatory requirements differ between regions
• Intent can be lost in translation

Current AI coordination assumes shared understanding. SEMA provides that understanding.

The Solution

SEMA (Semantic Meaning & Agreement) provides:

• Shared vocabulary across jurisdictions
• Context preservation (cultural, regulatory, temporal)
• Intent clarity beyond literal meaning
• Regulatory mapping (GDPR ↔ APPI ↔ POPIA)
• Cultural awareness in AI communication

Installation

pip install sema-protocol

With JIS (Jasper Identity Standard) integration:

pip install sema-protocol[jis]

Quick Start

Basic Term Translation

from sema_protocol import translate_term, RegulatoryRegion

# Translate "personal data" from EU to US context
result = translate_term(
    "personal data",
    RegulatoryRegion.EU,
    RegulatoryRegion.US,
    domain="compliance"
)

print(result)
# {
#     "status": "found",
#     "source_term": "personal data",
#     "target_term": "personal information",
#     "confidence": 0.95,
#     "regulatory_refs": ["CCPA 1798.140(o)"]
# }

Context-Aware Communication

from sema_protocol import SemanticContext, RegulatoryRegion, CulturalContext, align_contexts

# Dutch bank communicating with Japanese partner
nl_context = SemanticContext(
    region=RegulatoryRegion.EU,
    culture=CulturalContext.DIRECT,
    domain="finance",
    language="en"
)

jp_context = SemanticContext(
    region=RegulatoryRegion.JP,
    culture=CulturalContext.INDIRECT,
    domain="finance",
    language="en"
)

alignment = align_contexts(nl_context, jp_context)
print(alignment)
# {
#     "compatible": False,
#     "differences": [
#         {"aspect": "regulatory_region", "source": "eu", "target": "jp"},
#         {"aspect": "cultural_style", "source": "direct", "target": "indirect"}
#     ],
#     "adjustments": [
#         {"type": "communication_style", "guidance": "Soften direct statements..."}
#     ],
#     "risk_level": "medium"
# }

Domain-Specific Vocabulary

from sema_protocol import get_finance_domain, RegulatoryRegion

finance = get_finance_domain()

# Get fraud definition for South Africa
za_fraud = finance.get_fraud_definition(RegulatoryRegion.ZA)
print(za_fraud.definition)
# "Unlawful and intentional making of a misrepresentation resulting in prejudice"

print(za_fraud.regulatory_refs)
# ["Prevention of Organised Crime Act", "FIC Act"]

JIS Integration (Semantic Identity)

from sema_protocol import semantic_identity, SemanticContext, RegulatoryRegion

# Enrich a JIS identity with semantic context
jis_identity = {
    "actor_id": "bank_nl_001",
    "trust_score": 0.95,
    "intent": "Requesting fraud check on transaction",
    "capabilities": ["query", "alert"]
}

enriched = semantic_identity(
    jis_identity,
    context=SemanticContext(
        region=RegulatoryRegion.EU,
        domain="finance"
    )
)

print(enriched["semantic_layer"])
# {
#     "intent_clarity": {...},
#     "cross_border_ready": False,
#     "sema_version": "0.1.0"
# }

The Stack

SEMA is part of the HumoticaOS protocol stack:

┌─────────────────────────────────────┐
│  SEMA - Semantic Meaning Layer      │  <- You are here
├─────────────────────────────────────┤
│  JIS  - Identity + Trust + Intent   │
├─────────────────────────────────────┤
│  TIBET - Provenance + Audit         │
├─────────────────────────────────────┤
│  AInternet - Network + Discovery    │
└─────────────────────────────────────┘

• SEMA: What things mean in context
• JIS: Who is acting and their intent
• TIBET: Complete provenance chain
• AInternet: How agents find each other

Supported Regions

Region	Code	Key Regulations
EU	RegulatoryRegion.EU	GDPR, AI Act, AMLD6, PSD2
US	RegulatoryRegion.US	CCPA/CPRA, State laws
Japan	RegulatoryRegion.JP	APPI
Singapore	RegulatoryRegion.SG	PDPA
South Africa	RegulatoryRegion.ZA	POPIA, FIC Act
Australia	RegulatoryRegion.AU	Privacy Act
Brazil	RegulatoryRegion.BR	LGPD
Global	RegulatoryRegion.GLOBAL	Cross-border default

Cultural Contexts

Style	Code	Examples
Direct	CulturalContext.DIRECT	Dutch, German, Israeli
Indirect	CulturalContext.INDIRECT	Japanese, Korean, Thai
Contextual	CulturalContext.CONTEXTUAL	Chinese, Arabic
Neutral	CulturalContext.NEUTRAL	International business

Use Cases

Cross-Border Banking

AI systems from different banks need to coordinate on fraud detection. SEMA ensures "suspicious activity" means the same thing to both.

Global Compliance

Multinational companies need AI systems that understand GDPR in Europe, CCPA in California, and APPI in Japan simultaneously.

International Law Enforcement

When investigating cross-border crime, SEMA helps AI systems understand jurisdiction-specific legal definitions.

AI-to-AI Communication

As AI agents communicate globally, they need shared semantic understanding. SEMA provides the vocabulary.

Contributing

Contributions welcome! Please see our contribution guidelines.

License

AGPL-3.0-or-later - Because semantic infrastructure should be open.

Links

• Homepage: humotica.com
• Documentation: github.com/Humotica/sema-protocol
• Related: JIS | AInternet

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Part of HumoticaOS - One love, one fAmIly!