Semantica Framework - Performance Benchmarks
1. Processing Speed Benchmarks
1.1 Document Processing Speed
| Document Type |
Size |
Current (docs/hour) |
Target (docs/hour) |
Improvement |
| PDF |
10 pages |
1,200 |
5,000 |
+317% |
| DOCX |
5 pages |
2,500 |
10,000 |
+300% |
| HTML |
Articles |
5,000 |
20,000 |
+300% |
| JSON |
Structured |
10,000 |
50,000 |
+400% |
Test Environment: AWS c5.4xlarge (16 vCPU, 32GB RAM), Python 3.11
1.2 Entity Extraction Speed
| Model |
Current (entities/sec) |
Target (entities/sec) |
Improvement |
| Transformer-based NER |
450 |
1,500 |
+233% |
| spaCy NER |
600 |
2,000 |
+233% |
1.3 Triple Generation Speed
| Operation |
Current (triples/sec) |
Target (triples/sec) |
Improvement |
| Triple Extraction |
800 |
3,000 |
+275% |
| Graph Construction |
500 |
2,000 |
+300% |
2. Accuracy Metrics
2.1 Entity Extraction Accuracy
| Task |
Precision |
Recall |
F1 Score |
Target F1 |
| Entity Extraction |
0.94 |
0.91 |
0.92 |
0.95 |
| Relationship Extraction |
0.89 |
0.85 |
0.87 |
0.90 |
| Event Detection |
0.86 |
0.82 |
0.84 |
0.88 |
Test Dataset: CoNLL-2003, ACE 2005, custom domain datasets
2.2 Ontology Generation Quality
| Metric |
Current |
Target |
| Validation Score (F1) |
0.94 |
0.97 |
| Logical Consistency |
95% |
98% |
| Completeness |
88% |
92% |
2.3 Duplicate Detection Accuracy
| Metric |
Current |
Target |
| Precision |
0.97 |
0.98 |
| Recall |
0.95 |
0.97 |
| F1 Score |
0.96 |
0.98 |
3. GraphRAG Performance
| Approach |
Accuracy |
Latency |
Context Quality |
| Vector-Only RAG |
70% |
50ms |
⭐⭐⭐ |
| Graph-Only |
75% |
300ms |
⭐⭐⭐⭐ |
| Semantica GraphRAG (Hybrid) |
91% |
80ms |
⭐⭐⭐⭐⭐ |
Improvement: 30% accuracy increase over vector-only RAG with only 60% latency increase
4. Scalability Benchmarks
4.1 Large-Scale Processing
| Dataset Size |
Current Time |
Target Time |
Improvement |
| 100K documents |
48 hours |
12 hours |
4x faster |
| 1M documents |
480 hours (20 days) |
24 hours |
20x faster |
| 10M documents |
N/A (not feasible) |
240 hours (10 days) |
New capability |
4.2 Graph Size Limits
| Metric |
Current |
Target |
| Max Nodes (in-memory) |
10M |
100M |
| Max Edges (in-memory) |
50M |
500M |
| Query Latency (100M nodes) |
N/A |
<500ms |
5. Resource Usage
5.1 Memory Usage
| Operation |
Current (GB) |
Target (GB) |
Optimization |
| Processing 10K docs |
16 |
8 |
50% reduction |
| Graph storage (1M nodes) |
4 |
2 |
50% reduction |
5.2 CPU Utilization
| Operation |
Current |
Target |
| Single-threaded processing |
25% (1 core) |
80% (parallel) |
| Distributed processing |
N/A |
Linear scaling |
6. Real-Time Performance Targets
| Metric |
Current |
Target |
| Stream Processing Throughput |
N/A (batch only) |
10K events/second |
| Update Latency |
N/A |
<100ms |
| Query Latency (during updates) |
N/A |
<200ms |
7. Quality Assurance Metrics
7.1 Automated QA Performance
| Metric |
Current (Manual) |
Target (Automated) |
| Configuration Time |
4 hours |
5 minutes |
| Quality Score Accuracy |
85% (human-validated) |
90%+ (automated) |
| Domain Adaptation |
Manual per domain |
Automatic |
8. Comparison with Baselines
8.1 vs. Manual Knowledge Engineering
| Task |
Manual Time |
Semantica Time |
Speedup |
| Build KG (1K docs) |
40 hours |
2 hours |
20x |
| Generate Ontology |
20 hours |
30 minutes |
40x |
| Entity Resolution |
10 hours |
15 minutes |
40x |
9. Test Methodology
9.1 Test Environments
- Development: Local machine (16GB RAM, 8 cores)
- Testing: AWS c5.4xlarge (32GB RAM, 16 vCPU)
- Production Target: Distributed cluster (multiple nodes)
9.2 Test Datasets
- CoNLL-2003 (NER benchmark)
- ACE 2005 (Relation extraction)
- Custom domain datasets (healthcare, finance, legal)
- Synthetic large-scale datasets (1M+ documents)
10. Performance Improvement Roadmap
| Quarter |
Focus Area |
Expected Improvement |
| Q1 2025 |
Streaming architecture |
Real-time capability (new) |
| Q2 2025 |
Distributed processing |
5-10x speedup |
| Q3 2025 |
Optimization & caching |
2-3x additional speedup |
| Q4 2025 |
Production hardening |
Stability & reliability |
Document Version: 1.0 | Last Updated: 2025 | Semantica Framework Benchmarks