Yes, you can incorporate external knowledge graphs or ontologies into a generative AI model for better output by referring to the following:
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Embed Knowledge Graph Nodes: You can convert entities and relationships from the knowledge graph into embeddings and then integrate them into the model’s input layer.
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Retrieve Relevant Facts: You can use the knowledge graph to retrieve relevant facts based on the input prompt that will enhance the model’s context before generation.
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Contextual Prompting: You can also embed extracted knowledge as additional context in the prompt, which will guide the model in generating more accurate and fact-based responses.
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Fine-Tune with Graph Data: You can also fine-tune the model on text generated from the knowledge graph to help structure information and relationships.
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API Integration: You can also make query knowledge graphs in real-time during generation to provide the model with up-to-date facts, improving accuracy and relevance in outputs.
These strategies will help the model generate more informed, context-rich output by increasing structured external knowledge.