## Flowchart: LLM for KGC
### Overview
The flowchart illustrates the application of Large Language Models (LLMs) in Knowledge Graph Construction (KGC), divided into four main phases: Ontology Construction, Knowledge Extraction, Knowledge Fusion, and Future Applications. Each phase contains subcategories with specific methods, references, and color-coded distinctions.
### Components/Axes
- **Legend**:
- Red: Ontology Construction
- Orange: LLM-Driven Knowledge Extraction
- Green: LLM-Powered Knowledge Fusion
- Blue: Future Applications
- **Main Branches**:
1. **Ontology Construction** (Red)
- Top-down Ontology Construction
- Bottom-up Ontology Schema Construction
2. **LLM-Driven Knowledge Extraction** (Orange)
- Schema-based Methods
- Schema-free Methods
3. **LLM-Powered Knowledge Fusion** (Green)
- Schema-Level Fusion
- Instance-Level Fusion
- Comprehensive/Hybrid Frameworks
4. **Future Applications** (Blue)
- Knowledge Graph-based Reasoning for LLM
### Detailed Analysis
#### Ontology Construction (Red)
- **Top-down Ontology Construction**:
- Ontogenia (Lippiolis et al., 2025)
- CBcybCQ (Saedizade et al., 2024)
- Complementary prompting strategies (Lippiolis et al., 2025)
- **Bottom-up Ontology Schema Construction**:
- Natural Language-based Ontology Construction:
- LLM4OL (Giglio et al., 2023)
- N→OBL (translation) (Mateiu et al., 2023)
- NeoR-GPT (Fathaliha et al., 2025)
- LLM4LIn (Fathaliha et al., 2024)
- LKD-KGC (Sun et al., 2025)
- Data-to-Schema Foundations:
- GraphRAG (Edge et al., 2024)
- OntoRAG (Tiwar et al., 2025a)
- Structured Schema Induction:
- EDC (Zhang et al., 2024)
- AutoKGC (Ye et al., 2023)
- AutoSchemaKG (Bai et al., 2025)
#### LLM-Driven Knowledge Extraction (Orange)
- **Schema-based Methods**:
- Ontology-guided pipeline (Kommintz et al., 2024)
- KARMA (Lu et al., 2025)
- Ontology-grounded two-step extraction (Feng et al., 2024)
- OJKE (Khorshidi et al., 2025)
- Clinical extraction (Bhattarai et al., 2024)
- **Schema-free Methods**:
- Dynamic & Adaptive Schema-based Extraction:
- AutoSchemaKG (Bai et al., 2025)
- AdagKGC (Ye et al., 2023)
- Structured Generative Extraction:
- CoT-driven extraction (Nie et al., 2024)
- AutoRE (Kue et al., 2024)
- Retrieval-Augmented prompting (Papaioannou et al., 2024)
- CharIE (Wei et al., 2024)
- KOGEN (Niu et al., 2025)
- Open Information Extraction (OIE):
- EDC (Zhang et al., 2024)
#### LLM-Powered Knowledge Fusion (Green)
- **Schema-Level Fusion**:
- Ontology-driven consistency (Kommintz et al., 2024)
- LKD-KGC (Sun et al., 2025)
- EDC (Zhang et al., 2024)
- **Instance-Level Fusion**:
- KGGEIN (Mo et al., 2025)
- LLM-Align (Chen et al., 2024)
- EntGPT (Ding et al., 2025)
- RAG-based structural fusion (Pons et al., 2025)
- OMEM (Wang et al., 2024)
- **Comprehensive/Hybrid Frameworks**:
- KARMA (Lu et al., 2025)
- OJKE (Khorshidi et al., 2025)
- Graphfusion (Yang et al., 2024)
#### Future Applications (Blue)
- Knowledge Graph-based Reasoning for LLM:
- Dynamic Knowledge Memory for Agentic Systems
- Multimodal Knowledge Graph Construction
- New Roles for KGs in LLM Applications (Beyond RAO)
### Key Observations
1. **Temporal Progression**: Most methods are recent (2023–2025), indicating rapid advancements in LLM-driven KGC.
2. **Hybrid Approaches**: Comprehensive/Hybrid Frameworks (e.g., KARMA) integrate multiple techniques.
3. **Cross-Referencing**: Ontology Construction methods (e.g., LLM4OL) feed into Knowledge Extraction (e.g., AutoSchemaKG), which then enables Fusion (e.g., KGGEIN).
### Interpretation
The flowchart demonstrates a structured pipeline where LLMs enhance KGC at multiple stages:
- **Ontology Construction** provides foundational schemas using both top-down (knowledge-driven) and bottom-up (data-driven) approaches.
- **Knowledge Extraction** leverages LLMs to extract structured data, with schema-based methods relying on predefined ontologies and schema-free methods using generative extraction.
- **Knowledge Fusion** combines extracted knowledge at schema and instance levels, enabling comprehensive integration.
- **Future Applications** highlight emerging roles for KGs in LLM systems, emphasizing dynamic memory and multimodal reasoning.
The color-coded hierarchy underscores the interdependence of these phases, with Ontology Construction as the base layer enabling subsequent extraction and fusion processes. The emphasis on recent publications (2023–2025) reflects the field's rapid evolution, while the inclusion of diverse methods (e.g., RAG-based fusion, CoT prompting) illustrates the versatility of LLMs in KGC.
