Knowledge Search Example: Simple Search

Following is an example of a simple search through Search Widget and Luma Virtual agent (Luma Supported Channel).

In this example, the end-user is looking for information on resetting their Gmail account password. The search results in a list of relevant knowledge artifacts.

Search Widget

Knowledge Search in Search Widget is based on a Relevancy score. In the below example:

1. User searches for “How to reset account password.“

2. Luma Knowledge identifies 3 matching articles and calculates respective relevancy scores:

   • A1: “Change or reset your Gmail password” with a Relevancy score of 12.78

   • A2: “Reset a user's password using Azure Active Directory” and a Relevancy score of 11.42

   • A3: “Send password reset email for an Enterprise ID user” and a Relevancy score of 11.14

   • A4: “Create a new account in Azure Active Directory” and a Relevancy score of 5

3. Based on the following tenant configurations, results are displayed on the Search Widget:

All the Artifacts that fall in the Relevancy Score range, i.e., between the highest relevancy score for the matching artifacts (12.78) and the calculated Relevancy Threshold (7.66), are presented as the search result to the end-user. Relevancy Threshold is calculated as Score Range Percentage (tenant configuration) of the highest relevancy score (60% of 12.78 = 7.66).

The search result is displayed as:

Luma Virtual Agent

When the Artifact is searched through Luma virtual Agent or any of the Luma support Channels, Knowledge is displayed based on the following factors:

Confidence Band

Knowledge Searches through Luma-supported Channels are governed by Confidence Score. The Confidence Score of an Artifact indicates how confident the system is on the artifact to help the user with their inquiry. The metadata generated from the user query is matched against the metadata of the Artifacts available in Knowledge Base. If at least one phrase/value in the metadata type is matched, the respective score is awarded to the Artifact as the Confidence score. For more details on calculating Confidence score, refer to /wiki/spaces/KNOWLEDGEMANAGEMENT/pages/26049413514.

In the current example,

1. User searches for “How to reset account password.“

2. Luma Knowledge calculates the confidence score for the matched Artifacts (identified as per the relevancy score):

3. Now, based on the following tenant configurations, the matched Artifacts are classified in Confidence Bands. Consider that confidence thresholds for your tenant are as following:

Upper Confidence Score Threshold value for LOW Confidence level- 0.75
Upper Confidence Score Threshold value for MODERATE Confidence level - 0.58

The Artifacts are classified as:

The Highest Confidence Band of the identified Artifact is considered. For our search, the Knowledge Confidence Band is MODERATE.

User Intent

User intent is derived from the search phrase. It indicates if the user wants to view Knowledge or execute the predefined service.

For example:
“How to order a laptop” indicates that the user wants to view Knowledge Artifacts on how they can reset the password.
“Create a Service Request” indicates that the user wants help with a service that can reset the password.

For the search “How to reset account password“, user intent or pre-qualification is KNOWLEDGE.

Services on Luma VA

The user’s search request is also used to search predefined services available in Luma VA. Based on the Luma tenant configurations, the Virtual Agent identifies matching services and calculates the skill(s) confidence level. Services are presented to the users along with the Knowledge Artifacts if matching services with High or Moderate Confidence Levels are found. For more information on identifying Skills in Luma VA, refer to NLP Settings.

In the current example, let us consider that no pre-defined services are available in Luma VA. So, the Services’ confidence level is LOW.

Result Presentation preference in Luma VA

Based on the above factors, the system recommends if the end-user should be presented with Knowledge, predefined Service, or both. For our search, the system recommends displaying Knowledge Followed by Skills. For more information on rules for Knowledge and Services display, refer to Integration with Luma Knowledge.

However, based on your organization’s representation preference, the Luma VA administrator can customize the way Knowledge and Services are delivered to the end-user. You can configure Luma VA to always deliver Knowledge followed by Skill, Knowledge & Service together or honor DE recommendation. The configuration overrides the DE recommendation. For more information, Knowledge & Services Settings.

For the current example, let us consider that the Presentation preference for your Luma tenant is Default. This means the DE recommendation (Knowledge followed by Skill) is followed.

Based on the above factors, Luma VA will present Knowledge Artifacts to the end-user.