5 Reasons Chatbots Need a Strong Knowledge Base

By Harrison Clover   |   July 25, 2018
5 Reasons Chatbots Need a Strong Knowledge Base
It seems as if chatbots have taken over the internet. They are everywhere, and every chatbot claims artificial intelligence (AI).

What do artificial intelligence, natural language processing, and machine learning mean in the realm of chatbots? How do you ensure that your chatbot is intelligent, and how does your knowledge base fit in to the picture?

Today, most people experience a chatbot first with virtual assistants such as Alexa, Cortana, or Siri. These use their wealth of knowledge to provide comprehensive answers to your simple queries. They convert input text into a structure to convert it into an internal query, obtain an output, then deliver that output with a text-to-speech exchange. Their capability to continually provide relevant information to simple queries is why they seem really intelligent. But, without that knowledge base to draw upon, they are simple apps.

Let’s first take a look at what function chatbots play, the definitions of their intelligence and where it comes from, and the critical importance of your knowledge base in providing a platform from which that intelligence springs.

Agency in Chatbots

Most likely, you don’t view your chatbot as an agent. However, this is an important characteristic. The agent must be able to proceed toward a goal autonomously. Defining that goal in specific circumstances can be complex and hold varying importance depending upon the chatbot. For instance, some situations may require models that simulate social interactions and emotions, rather than just rational answers to queries.

Learning enables these chatbot agents to observe patterns in the data they receive and respond correctly to those patterns. This goes beyond artificial intelligence alone.

Defining Your Chatbot’s Intelligence

A chatbot proceeds through a cycle of sense-think-act, although for a chatbot, sensing the environment is reading the sentences typed into the text field. As each agent moves towards its objective through the sense-think-act model, it first gathers the information necessary to complete a task by sensing the environment. The next step involves thinking. This represents the essential substance of AI. The data must be converted to a form the chatbot can use for reasoning. The preexisting and new data must be updated. Then the chatbot must form a decision based upon this updated knowledge base and convert the decision into an action to be executed by an actuator.

The Role of Deep Learning

With today’s technology, we know more about deep learning and neural networks, which gives us a broader view of how learning occurs. As noted above, intelligent chatbots built on a strong knowledge base can identify patterns and respond appropriately. However, this happens during the thinking process. What else can an intelligent chatbot accomplish? This first part is converting that data sense into usable information.

How does this happen? Natural language processing (NLP) and comprehension are the elements of AI that concern chatbots. Although significant progress has happened in this area, chatbots still need the expanding knowledge base for quality answers. Between natural language processing and the learning component lies the knowledge base, and it is necessary for an intelligent chatbot. How the data is stored is extremely important, and it determines the nature of learning that occurs and the level of intelligence the chatbot is capable of showing.

The final step in the cycle is for the chatbot to determine what action to take based upon the knowledge gathered and learned. Specifically, this is determining what response the chatbot will deliver to the inquirer. This may be more than a single step. A more intelligent chatbot may actually be able to plan out a few steps and ask a series of questions that may modify the final response.

For instance, the reason that Siri, Cortana, Google Now, and others appear intelligent is a result of how the information is represented internally. Because of this, the virtual assistance learns faster, determines which data is relevant, selects the most relevant information, analyze it, and provide an answer.

When your knowledge is stored appropriately with the correct rules and data structures, it is tremendously powerful, even positively impacting the learning that occurs.

Is Every Chatbot Intelligent?

When you communicate through a messenger to a machine using an algorithm, and that messenger responds, then you are communicating with a chatbot. Is that chatbot intelligent? Again, it depends upon the strength of the knowledge base. In some ways, it does act intelligently in that it receives a command and responds based upon algorithms. Its objective is to respond as an agent. However, this is too simplistic and even a bit nit picky.

The more accurate answer is that responses that use AJAX (asynchronous JavaScript and XML) in conducting some validation is intelligent. In this instance, the chatbot must go through the sense-think-act cycle, but with the objective of gathering additional details from you or the user. However, this is still not the most sophisticated intelligence.

Some bots have buttons to click and respond with a query. Are these artificially intelligent? Yes, although they have a ways to go on the conversation axis.

Do we need to define a chatbot’s intelligence? You will find many scholarly articles on what makes a chatbot appear intelligent. It can be NLP that produces comprehension of sentences that contain grammatical and spelling errors. It could be a sophisticated conversational interface that is easy to use and understand. Some people thing chatbot intelligence is displayed by answering off-topic questions correctly. Having a memory is good, but the chatbot must learn and evolve over time, otherwise it doesn’t display intelligence.

Some of the more sophisticated chatbots today, such as x.ai, Google assistant, and mimetic.ai, are constructed-upon intelligent platforms that are effective even with no NLP component. While this component is important, the platform, or knowledge base, is the powerful tool that enables learning.

The Role of Your Knowledge Base

This is truly where a well-designed knowledge base can positively impact the effectiveness of your chatbots providing one voice across all your channels by using a Knowledge base that will delivering unified messages to your customers making them happier, and your knowledge base improves as more learning occurs.

Thus, your challenge in creating an intelligent, highly effective chatbot begins with creating an intelligent knowledge base that can relate to and solve real world problems. Focus on creating this intelligent knowledge base with clearly defined objectives and a clear understanding of the sense-think-act cycle of your chatbot.