Kore.ai’s Knowledge Graph helps you turn your static FAQ text into an intelligent, personalized conversational experience. It goes beyond the usual practice of capturing FAQs in the form of flat question-answer pairs. Instead, Knowledge Graph enables you to create an ontological structure of key domain terms and associate them with context-specific questions and their alternatives, synonyms, and Machine learning-enabled traits. This Graph, when trained by the platform, enables an intelligent FAQ experience.
Why Knowledge Graph
Any query can be expressed in multiple ways by the user. It is a difficult task for you to visualize and add all the alternative questions manually.
Kore.ai designed Knowledge Graph with nodes, tags, and synonyms which would make the work easier for you to cover all the possible matches. The Knowledge Graph can handle various alternate questions with the training using the nodes, tags, and synonyms.
Whenever a question is asked by the user and if no intent matches it, then the node names in the Knowledge Graph will be checked and matched with the keywords from the question. Node names, tags, and synonyms will be checked and based on the score, the best possible intent will be presented to the user which can take the form of either a simple response or execution of a dialog task.
This way, you can add a very few completely different alternative questions in the FAQ and provide tags, synonyms and node names appropriately such that any untrained question can also be matched. The performance and intelligence of the Knowledge Graph depend on the way you train it with the appropriate node names, tags, and synonyms. See below for an explanation on the terms.
Once the Knowledge graph is configured by defining terms, synonyms, primary and alternative questions, and answers, the user-provided utterance will only be indexed from the FAQs present under the qualified path. The FAQs presented to the user based on the provided utterance completely rely on the following parameters:
- Path Coverage – Define the minimum percentage of terms in user’s utterance to be present in a path to qualify it for further scoring.
- Definite Score for KG – Define minimum score for a KG intent match to be considered as a definite match and discard any other intent matches found.
- Minimum and Definitive Level for Knowledge Tasks – Define minimum and definitive threshold to identify and respond in case of a Knowledge task.
- KG Suggestions Count – Define the maximum number of KG / FAQ suggestions to be presented when definite KG intent match is not available.
- The proximity of Suggested Matches – Define the maximum difference to be allowed between top-scoring and immediate next suggested questions to consider them as equally important.
While the platform provides default values for the above-mentioned thresholds, these can be customized by the developer. See here for how to set the threshold levels.
This document is intended to familiarize the reader with the terms used in building Knowledge Graph.
Terms or Nodes
Terms or Nodes are the building blocks of an ontology and they can be used to define the fundamental concepts and categories of a business domain.
As shown in the image below, you can organize the terms on the left-hand panel of the Bot Ontology window in a hierarchical order to represent the flow of information in your organization. You can create, organize, edit, and delete terms from there.
For easier representation, we identify some special nodes using the following names:
Root node forms the topmost term of your Bot Ontology. A Knowledge Graph consists of only one root node and all other nodes in the ontology become its child nodes. Root node takes the name of the bot by default, but you can change it later.
The immediate next level nodes of the root node are known as First-level nodes. There can be any number of First-level nodes in a collection. It is recommended to keep First-level nodes to represent high-level terms such as the names of departments or functionality. Examples – Personal Banking, Online Banking, and Corporate Banking.
Any node to which question-answer set or dialog task is added is called a Leaf Node, be it at any level.
Depending on their position in the ontology, a node can be referred to as first-level nodes, second-level nodes, etc.. A first-level node is in simple terms a category that may have one or more subcategories under it, called the second-level nodes.
Examples: Loan is the first-level node of Home Loan and Personal Loan. Personal Loan can again have two subcategory nodes: Rate and Fees, Help and Support.
Users would use a variety of alternatives for the terms of your ontology. Knowledge Graph allows you to add synonyms for the terms to include all possible alternative forms of the terms. Adding synonyms also reduces the need for training the bot with alternative questions.
For example, the Internet Banking node may have the following synonyms added to it: Online Banking, e-banking, E-banking, Cyberbanking, and Web banking.
When you add a synonym for a term in the Knowledge Graph, you can add them as local or global synonyms. Local synonyms apply to the term only in that particular path, whereas global synonyms apply to the term even if it appears on any other path in the ontology.
Note: From ver 7.0, Traits replace Classes of ver 6.4 and before.
A trait is a collection of typical end-user utterances that define the nature of a question when they ask for information related to a particular node. A trait is a common feature that can be applied to multiple terms across your Bot Ontology.
Bot Response can either be execution of a Dialog Task or an answer.
The question-answer pairs must be added to relevant nodes in your bot ontology. A question may be asked differently by different users and to support this, you may associate multiple alternate forms for each question.