GETTING STARTED
Kore.ai XO Platform
Virtual Assistants Overview
Natural Language Processing (NLP)
Concepts and Terminology
Quick Start Guide
Accessing the Platform
Working with the Builder
Building a Virtual Assistant
Using Workspaces
Release Notes
Current Version
Previous Versions
Deprecations

CONCEPTS
Design
Storyboard
Dialog Tasks
Overview
Dialog Builder
Node Types
Intent Node
Dialog Node
Entity Node
Form Node
Confirmation Node
Message Nodes
Logic Node
Bot Action Node
Service Node
Webhook Node
Script Node
Group Node
Agent Transfer
User Prompts
Voice Call Properties
Dialog Task Management
Connections & Transitions
Component Transition
Context Object
Event Handlers
Knowledge Graph
Introduction
Knowledge Extraction
Build Knowledge Graph
Add Knowledge Graph to Bot
Create the Graph
Build Knowledge Graph
Add FAQs
Run a Task
Build FAQs from an Existing Source
Traits, Synonyms, and Stop Words
Manage Variable Namespaces
Update
Move Question and Answers Between Nodes
Edit and Delete Terms
Edit Questions and Responses
Knowledge Graph Training
Knowledge Graph Analysis
Knowledge Graph Import and Export
Importing Knowledge Graph
Exporting Knowledge Graph
Creating a Knowledge Graph
From a CSV File
From a JSON file
Auto-Generate Knowledge Graph
Alert Tasks
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Digital Skills
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Views
Introduction
Panels
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Train
Introduction
ML Engine
Introduction
Model Validation
FM Engine
KG Engine
Traits Engine
Ranking and Resolver
NLP Configurations
NLP Guidelines
Intelligence
Introduction
Contextual Memory
Contextual Intents
Interruption Management
Multi-intent Detection
Amending Entities
Default Conversations
Sentinment Management
Tone Analysis
Test & Debug
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Utterence Testing
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Deploy
Channels
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Analyze
Introduction
Conversations Dashboard
Performance Dashboard
Custom Dashboards
Introduction
Meta Tags
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Conversation Flows
NLP Metrics
Containment Metrics
Usage Metrics
Smart Bots
Universal Bots
Introduction
Universal Bot Definition
Universal Bot Creation
Training a Universal Bot
Universal Bot Customizations
Enabling Languages
Store
Manage Assistant
Plan & Usage
Overview
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Invoices
Authorization
Multilingual Virtual Assistants
Masking PII Details
Variables
IVR Settings
General Settings
Assistant Management
Data Table
Table Views
App Definitions
Sharing Data Tables or Views

HOW TOs
Build a Flight Status Assistant
Design Conversation Skills
Create a Sample Banking Assistant
Create a Transfer Funds Task
Create a Update Balance Task
Create a Knowledge Graph
Set Up a Smart Alert
Design Digital Skills
Configure Digital Forms
Configure Digital Views
Add Data to Data Tables
Update Data in Data Tables
Add Data from Digital Forms
Train the Assistant
Use Traits
Use Patterns for Intents & Entities
Manage Context Switching
Deploy the Assistant
Configure an Agent Transfer
Use Assistant Functions
Use Content Variables
Use Global Variables
Web SDK Tutorial
Widget SDK Tutorial
Analyze the Assistant
Create a Custom Dashboard
Use Custom Meta Tags in Filters

APIs & SDKs
API Reference
API Introduction
API List
API Collection
koreUtil Libraries
SDK Reference
SDK Introduction
SDK Security
SDK Registration
Web Socket Connect and RTM
Using the BotKit SDK

ADMINISTRATION
Introduction
Assistant Admin Console
Administration Dashboard
User Management
Add Users
Manage Groups
Manage Roles
Assistant Management
Enrollment
Invite Users
Send Bulk Invites
Import User Data
Synchronize Users from AD
Security & Compliance
Using Single-Sign On
Security Settings
Cloud Connector
Analytics
Billing
  1. Home
  2. Docs
  3. Virtual Assistants
  4. Natural Language
  5. Traits

Traits

In natural conversations, it is very common that a user provides background/relevant information while describing a specific scenario. Traits are specific entities, attributes, or details that the users express in their conversations. The utterance may not directly convey any specific intent, but the traits present in the utterance are used in driving the intent detection and bot conversation flows.

For example, the utterance my card is being rejected and am on a business trip expresses two traits card decline and emergency. In this scenario, the utterance does not convey any direct intent, or at best it is used to trigger the unblock card flow. However, the presence of emergency trait is used to directly assign the conversation to a human agent.

Traits feature of the Bots platform is aimed at identifying such characteristics present in user utterances and use them for intent detection and in customizing the bot definition using these characteristics.

Use Case

Book a Flight bot might have an added requirement to book a flight based on the cost preference.

User utterance: I am looking for a low-cost option to London must result in ordering the available flights and picking the lowest-priced ticket.

This can be achieved by:

  • Adding a Trait Type called Travel Class with Trait Economy trained with the utterance low cost.
  • Adding a Rule for book flight to be triggered in the presence of Economy Trait.
  • Add transition condition in case Trait Economy is present in the context.

Configuration

Configuring Traits involves:

  • Trait Definition
  • Trait Association Rules
  • Trait Detection

Trait Definition

To define Traits, under the Build top menu option, from the left menu, click Natural Language –> Training and select the Traits tab.

Following are the key features to be considered while defining Traits:

  1. Trait Type is a collection of related traits like Travel Class in the above example.
  2. Trait Type can be ML Based or Pattern Based. Each trait of a trait type can be trained using words, phrases, utterances, or patterns based on the type. Manage Trait Type allows you to define the training configuration. See below for ML-based trait configuration.
  3. A Trait Type can have one or more Traits.
  4. Traits names should be unique in a group. But traits with the same name can be present in multiple groups.
    • For ML-based Traits, you can define the words, phrases, or utterances that identify the trait. One trait per trait type is detected for ML-based trait types.
    • For Pattern-based Traits, you can define the patterns associated with the given trait. There is a possibility of multiple traits getting detected for pattern-based trait types. Ordering of Traits within the Trait Type signifies the importance of a trait in a trait type and detects only one trait.
  5. Once added, Train the bot for the Traits to be detected from user utterances.

 

Notes:

  1. You can add language-specific traits in the case of multi-lingual bots.
  2. When a trait name is modified, ensure that all the rules defined using that trait are corrected. This has to be done manually, the platform will not take care of it.
  3. The trait name must be unique in a group.
  4. Traits with the same name can be present in multiple groups, but distinguishing them in trait rules or trait detection results is difficult.

Traits – ML Model

When choosing to train traits using the ML model, by default, the n-gram model is used. An n-gram is the contiguous sequence of words used from training sentences to train the model. But this might not be effective when the corpus is very less or when the training sentences, in general, contain fewer words.

From v8.0 of the platform, an option is included to skip or use the n-gram model. Further, the option to parameterize the n-gram algorithm is included.

  • When the n-gram option is selected, you can configure the n-gram Sequence Length by setting the maximum value of the n-gram. It is set to 1 by default and can be configured to any integer value between 1 and 5.
  • When skip-gram is selected, you can configure
    • Sequence Length specifying the number of words to be included in a non-consecutive sequence. It is set to 2 by default and it can take any integer value between 2 and 4.
    • Maximum Skip Distance for the number of words that can be skipped to form a non-consecutive sequence of words. This value is set to 1 by default and can take any integer value from 1 to 3.

NOTE: While the settings are same for all languages (in case of multilingual bot), for some languages like Chinese and Korean sequence of characters form grams and for other (Latin-based) languages are word grams.

Trait Association Rules

Trait Rules define Dialog Execution and Knowledge Graph Intent detection.

Dialog Execution

Intent detection or Dialog execution is achieved using traits, along with the ML utterances and patterns. To achieve this, intent must be associated with the required traits by adding Rules.

There are multiple ways to add rules:

  1. From the Traits section using the Add New Rule link.
  2. From the Intent Node using the Rules section under the NLP Properties.
  3. From the Rules tab for a given Intent.

Each rule can have one or more conditions with AND as the operator. Multiple trait rules can be defined for a given intent and the intent is considered as a definite match if any one of the rules matches.

Knowledge Graph Intents

Knowledge Graph can also be part of the discovery process using Traits. For this, each term or node can be associated with a trait. A given term can be associated with a single Trait.

Trait Detection

Only one trait from a group (trait type) will be detected and is considered as a definite match.

Traits detected are included in the context object. The context is populated with unique traits identified (without reference to trait type). This information can be used in:

  • Intent identification
  • Dialog transition
  • Entity population
  • bot definitions

Batch Testing reports also include information about traits detected as do the Find Intent API.

Intent Detection

The Ranking and Resolver gets input from the three NL engines and Traits to analyze and come up with the possible/definitive matches.

  • The intent is considered as a definite match only if all the traits (one in the case of Knowledge Graph) present in a trait rule are detected.
  • NL Analysis includes information on traits detected and the NLP Flow shows the information about traits detected.

Dialog Transition

Conversation Flow is controlled using Traits. For a Dialog, Connection Rules are defined using the Trait Context. This is done from the Connection tab under the Properties Panel for the Dialog.

The Traits Context is accessed using context.traits. It returns an array of all traits matching the intent, hence the condition to be used is contains.

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