Knowledge Graph Overview

Documentation Index

Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

Back to NLP Topics A Knowledge Graph (KG) converts static FAQ text into a structured conversational experience. It supports two models: an ontology-based model using hierarchical terms, and an LLM-based Few-Shot model that requires no ontology.

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Knowledge Graph Types

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Ontology Knowledge Graph

Organizes FAQs using terms, synonyms, traits, and context. When a user asks a question, the engine matches utterance tokens against KG nodes (path qualification), then scores shortlisted questions using cosine similarity. Enable: Go to Automation AI > Natural Language > NLU Config > Engine Tuning > Knowledge Graph and select Ontology Model. How it works:

1. User utterance and KG nodes are tokenized; n-grams extracted (up to quad-gram).
2. Tokens are mapped to KG nodes to get indices.
3. Path qualification: paths are shortlisted based on term coverage and mandatory term presence.
4. Best match is selected by cosine scoring over shortlisted questions.

Training process:

1. All terms/nodes and their synonyms are indexed.
2. A flattened path is established for each KG intent using those indices.

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Few-Shot Knowledge Graph

Uses Kore.ai’s LLM to identify FAQs by semantic similarity—no ontology needed. Add all FAQs to the root node. Enable: Go to Automation AI > Natural Language > NLU Config > Engine Tuning > Knowledge Graph and select Few-Shot Model. Prerequisites before enabling:

• Requires NLP V3 and Ranking & Resolver V2 (auto-updated when enabled).
• Embedding model options: BGE M3 and Pretrained MPNet.
• When switching from Ontology KG: Default terms are retained until updated, then become Organizer terms (can be set as Mandatory).
• Only Mandatory terms support path-level synonyms.

How it works: The LLM computes semantic similarity between the user utterance and FAQs, returning a similarity score. The score determines match type (definite, probable, etc.) based on thresholds. Matched intents are sent to Ranking and Resolver to select the winner.

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Embeddings Model Accuracy

Model accuracy across multiple languages using a single dataset, enabling a comparative evaluation of multilingual performance. Model accuracy for English across different datasets from multiple domains, providing a concise summary for quick comparison.

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Selecting Your KG Type

Starting with v10.1, Few-Shot is the default for new KGs under NLP V3 in English. Go to Automation > Knowledge AI > FAQs to switch types.

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Feature Comparison

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