Building A Knowledge Graph-Based Chatbot

When we think of bots, we either think of Star-Wars-like robots or humanoid androids communicating with us.

What are chatbots? Chatbots are computer programs built to communicate with humans using natural language. Usually, they handle customer service tasks, such as answering questions and booking appointments.

The primary purpose of chatbot systems is to retrieve valuable and relevant information from one or multiple knowledge bases by using natural language understanding (NLU) and semantic web technologies. 

The problem

The web is packed with the most disparate information; it’s often hard for humans to classify and judge the quality of a source at first sight, so, in order to save time, which is our most valuable asset, we turn to machines to do the searching for us.

Technically speaking

Many chatbot systems are designed to require a lot of training data, which is unavailable and expensive to create. Recently, with the growth development of linked data, increasing progress on chatbots has been seen in research and industry. However, they face many challenges, including user query understanding, intent classification, multilingual aspect, multiple knowledge bases, and analytical queries interpretation.

Knowledge graphs are becoming more and more a standard technology for Companies, people, and publishers. A Knowledge-Graph-based bot is scalable, flexible, and dynamic.

Why Knowledge Graphs

A Knowledge Graph is a structure that portrays information as relationships and concepts connected to each other. Google was the first to mention Knowledge Graphs and the technology behind them back in 2012. 
You can find out more about why you need a Knowledge Graph, read our blog post.

Back to us now!

So we know that a chatbot needs a domain model in order to understand and answer user questions. In machine learning, we would generate a huge knowledge base of sentences and use cases, wasting time and resulting in a static chatbot, which the web is full of already.

Since a domain model collects information about a particular topic or subject, we can use a Knowledge Graph to store and retrieve information easily.

In this way, our chatbot would be dynamic and always up to date without manually adding and editing the knowledge base.

Advantages of Knowledge Graph-Based Chatbots

A chatbot based on a Knowledge Graph knows how to interpret requests from the users delivering meaningful answers straight away. Moreover, there won’t be a need for lengthy training. 

Knowledge Graphs can be updated more efficiently simply by adding data and relationships to other entities.

How To Develop A Knowledge-Based Chatbot

First of all, there is the preparatory work for the Knowledge Graph, in which there should be a clear structure of concepts, entities, and relationships between them.

Then, the chatbot needs to “study” those rules, entities, and relations to answer questions.

These are three main flows that describe how to develop a conversational interface based on a Knowledge Graph: 

• Pushing contents from the Knowledge Graph (KG) to the website/backend (via GraphQL interface) – the data present in the KG are made available through targeted queries.
• Semantic content search on questions-answers (via search API) – the FAQ content is made available through targeted queries that can be consulted performing semantic search, which allows you to manage, if present, the intent associated with specific questions.
• Natural language understanding and dialogue management (via NLU for intent management) – the KG data and the semantic search functions are operated by an external dialogue manager application that intercepts the user’s intent and retrieves the answers (accessing content either in the KG or from semantic search).

Knowledge Graph and GraphQL

The Knowledge Graph is fed with content flows from the CMS and through the NLP end-point, which analyzes the unstructured contents and organizes them semantically using the Company’s vocabulary containing the key concepts (entities).

The graph may contain different content types: FAQpages, NewsArticles, Articles, Offers… all useful to sustain a conversation flow that includes different nuances on the same topic, as a user might want to know a definition of a term or articles related to this term.

GraphQL is a query language and execution engine designed to implement API-based solutions that access information in the Knowledge Graph.

We use GraphQL as a query service, and its functions are available for data consultation only. Reading takes place through a different end-point.

GraphQL is used when users want to see all articles related to a topic.

Requests towards GraphQL are executed in the Knowledge Graph via authentication.

Semantic Search

This module integrates the API for semantic search to the knowledge base. A first usage scenario is based on the FAQ content present on the site today.

Later, it will be possible to extend the scope of use of this feature to all web content in the KG through the use of models for open-domain question answering (ODQA).

The search for answers (/ ask)

The answers to user questions are extracted through a semantic content search system.

This way, we can enable a classification algorithm that uses a transformer-based neural network to classify questions based on how “significant” they are in relation to the user’s query.

The use case is based on the following application flow:

• The dialogue manager (NLU) identifies an intent that can be satisfied by FAQ content
• The user’s question is delivered to the Search API
• The Search API identifies the closest question-answer
• The result is sent to the dialogue manager (NLU)
• The dialogue manager via ECSS sends the response to the end-user

Understanding of natural language and dialogue management

We use a scalable architecture: the two main components are natural language understanding (NLU – Natural Language Understanding) and a dialogue management platform.

NLU deals with managing the classification of intentions, the extraction of entities, and the recovery of responses. In the diagram, it is represented as an NLU pipeline because it processes user expressions using an NLU model generated by the trained pipeline.

The dialogue management component determines the following action in a conversation based on context (the Dialogue Policies indicated in the diagram).

Conclusions

Knowledge Graphs are very powerful databases to develop a conversational marketing strategy, and when it comes to building a conversational interface, they stand out from other technologies in terms of:

• Speed
• Powerful interrelationships between concepts 
• Flexibility 
• Possibility to easily update the graph
• Embed potentially unlimited knowledge 

The goal of any bot is to provide valuable and relevant information to users, adapting to each context and request. And that’s what Knowledge Graphs do: they move and adjust.

In a world of infinite information, be the KG-powered bot!