OVERVIEW
Virtual Assistants
Kore.ai Platform
Key Concepts
Natural Language Processing (NLP)
Accessing Platform
VIRTUAL ASSISTANTS
Virtual Assistant Builder
Virtual Assistant Types
Getting Started
Creating a Simple Bot
SKILLS
Storyboard
Dialog Task
Introduction
Dialog Builder (New)
Dialog Builder (Legacy)
User Intent Node
Dialog Node
Entity Node
Supported Entity Types
Composite Entities
Supported Colors
Supported Company Names
Form Node
Logic Node
Message Nodes
Confirmation Nodes
Bot Action Node
Service Node
Custom Authentication
2-way SSL for Service nodes
Script Node
Agent Transfer Node
WebHook Node
Grouping Nodes
Connections & Transitions
Manage Dialogs
User Prompts
Knowledge Graph
Terminology
Building
Generation
Importing and Exporting
Analysis
Knowledge Extraction
Build
Alert Tasks
Introduction
Ignore Words and Field Memory
How to Schedule a Smart Alert
Small Talk
Digital Views
Overview
Configuring Digital Views
Digital Forms
Overview
How to Configure Digital Forms
NATURAL LANGUAGE
Overview
Machine Learning
Introduction
Model Validation
Fundamental Meaning
Introduction
NLP Guidelines
Knowledge Graph
Traits
Introduction
How to Use Traits
Ranking and Resolver
Advanced NLP Configurations
INTELLIGENCE
Overview
Context Management
Overview
Session and Context Variables
Context Object
How to Manage Context Switching
Manage Interruptions
Dialog Management
Sub-Intents & Follow-up Intents
Amend Entity
Multi-Intent Detection
Sentiment Management
Tone Analysis
Sentiment Management
Event Based Bot Actions
Default Conversations
Default Standard Responses
TEST & DEBUG
Talk to Bot
Utterance Testing
Batch Testing
Conversation Testing
CHANNELS
PUBLISH
ANALYZE
Overview
Dashboard
Custom Dashboard
Overview
How to Create Custom Dashboard
Conversation Flows
NLP Metrics
ADVANCED TOPICS
Universal Bots
Overview
Defining
Creating
Training
Customizing
Enabling Languages
Store
Smart Bots
Defining
koreUtil Libraries
SETTINGS
Authorization
Language Management
PII Settings
Variables
Functions
IVR Integration
General Settings
Management
Import & Export
Delete
Versioning
Collaborative Development
PLAN & USAGE
Overview
Usage Plans
Support Plans
Invoices
API GUIDE
API Overview
API List
API Collection
SDKs
SDK Overview
SDK Security
SDK App Registration
Web SDK Tutorial
Message Formatting and Templates
Mobile SDK Push Notification
Widget SDK Tutorial
Widget SDK – Message Formatting and Templates
Web Socket Connect & RTM
Using the BotKit SDK
Installing
Configuring
Events
Functions
BotKit SDK Tutorial – Agent Transfer
BotKit SDK Tutorial – Flight Search Sample Bot
Using an External NLP Engine
ADMINISTRATION
HOW TOs
Creating a Simple Bot
Creating a Banking Bot
Context Switching
Using Traits
Schedule a Smart Alert
Configure UI Forms
Add Form Data into Data Tables
Configuring Digital Views
Add Data to Data Tables
Update Data in Data Tables
Custom Dashboard
Custom Tags to filter Bot Metrics
Patterns for Intents & Entities
Build Knowledge Graph
Global Variables
Content Variables
Using Bot Functions
Configure Agent Transfer
Update Balance Task
Transfer Funds Task
RELEASE NOTES
  1. Docs
  2. Virtual Assistants
  3. Builder
  4. Knowledge Graph
  5. Taxonomy-based KG

Taxonomy-based KG

The Kore.ai platform provides a Taxonomy-based model in the Knowledge Graph to enhance the path qualification.

The default Knowledge Graph model works on a two-step model i.e. path qualification and followed by question matching. The path need not be fully qualified at all times. Even a partial path match (above a threshold) is considered as a qualification and the questions in these paths are used for matching the user input.

In the ‘taxonomy’ based approach, the ‘path’ should fully match at all times. The idea here is that every term in the path is equally important and only a full match of all the terms in the path should be considered as a qualification. Once a path is qualified, the questions in that path or paths will be considered for intent identification against user input. This is useful when you want to ensure that FAQs are answered from a given path only when the complete path is either present in the user’s input or disambiguated from the user to answer the most confident questions.

To achieve this, the platform allows a custom configuration settings Taxonomy-based Knowledge Graph under the Advanced NLP Configurations. By default, this setting is disabled. When enabled, the Knowledge Graph evaluates FAQs using the taxonomy-based approach.

This feature was released with v9.0 of the platform and is in (beta) state.

Use Case

Consider the following Knowledge Graph:

Any query related to accounts – opening or closing would yield in the following:

With Taxonomy enabled KG, even partial match of the node/terms will yield in a response that is useful:

Advantages

  1. Taxonomy-based KG helps disambiguate at the parent level. Nodes/terms are considered more important in the KG qualification.
  2. It allows for multiple questions within one leaf by allowing you to link existing questions to new intents.
  3. Allows you to give display names for nodes for presenting to the user.

Enabling

To enable the Taxonomy-based custom configuration settings, follow the below steps:

  1. Log in to the Kore.ai platform with valid credentials.
  2. By default, the Bot Summary page is displayed.
  3. On the left pane, click Natural Language -> Thresholds & Configurations.
  4. On the Thresholds & Configurations page, click Advanced NLP Configurations. You can configure the thresholds and configurations associated with the advanced NLP settings.
  5. In the Select a Configuration drop-down field, select Add Custom from the list.
  6. In the Custom Configuration field, type KG_Taxonomy_Based and click Enter.
  7. In the Configuration Value field, type enable and click Enter to save the configuration.

Features

Once you enable the Taxonomy-based KG, the following options are enabled:

  1. For each node/term, you can:
    • enter the Term Display Name – this is presented to the user for disambiguation
    • enable the Auto Qualify Path – this will ensure that the term is auto-qualified if any of its immediate child terms are qualified.
  2. When adding a new intent, you have the option to Link an Existing Question – this might  prove useful since the user might provide a partial input that contains one or more terms

Notes

Once you enable the Taxonomy-based KG, keep in mind the following:

  1. For the Ranking and Resolver engine the following settings would be disabled:
    • Prefer Definitive Matches, and
    • Rescoring of Intents
  2. The following settings of the Knowledge Graph engine would be ignored:
    • Path Coverage,
    • KG Suggestions Count, and
    • Proximity of Suggested Matches.
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