In this age of information, knowledge is power but only if you can access it when you need it. Knowledge-based chatbots makes this power available at your fingertips.
Knowledge-based chatbots are type of AI chatbots that relies on structured repository of information which we then call it a knowledge-base to provide precise, and contextually relevant response.
Unlike generic conversational AI like GPTs, these chatbots are built specifically to answer question based on established knowledge rather than purely conversational intents.
Lets check on this examples:
Looking at a knowledge-based chatbot for university, this chatbot could have an ability to answer questions like:
“What are the admission requirements for the Computer Science program?”
“Who is the principal of Engineering collage“
These responses are directly sourced from the university’s structured information, meaning chatbot should have access to the internal university’s information to give responses to these questions.
How Knowledge-Based Chatbots Work
- Knowledge Sources
Knowledge-based chatbots rely on the centralized data repositories such as:
FAQ’s for simple question and answers mapping
Documents and manuals for technical queries or
Knowledge graphs to handle more complex relationships (i.e., What is the capital city of Tanzania?, pull data from the graph linking countries and capitals).
Example Tool: A chatbot built on Elasticsearch can index and search a company’s document repository to instantly provide relevant passage as answers.
- Natural Language Processing(NLP) components
Intents recognition: Chatbots needs to determine what the user wants to know (e.g., “What are the requirements for admission in Computer Science Course“). User is demanding for a list of requirements which will make him or her to qualify for the admission, (Human can process language, so Knowledge-based chatbots have the same ability too)
Entity Extraction: Here Knowledge-based chatbot identifies key pieces of information in the query, such as dates, product name, or categories, enabling it to quickly search for related contents.
- Information Retrieval
Once question is already understood, the chatbot uses query matching or semantic search to retrieve the most relevant answers form the knowledge base (repository).
Lets explore an example:
if user asks: “What are the warranty terms for a MacBook“ the chatbot will:
Recognize the intents as “warranty inquiry.“
Matching the entity “MacBook“ in the knowledge base.
Retrieve and display the specific warranty policy for MacBook Products.
There we go, we get a straight forward answers from the chatbot about the Warranty Policy.
- Context Management
This is very important in handling multi-turn conversations:
- If user follows up with a question like “Does it cover water damage?”, the chatbot connects this query to prior question about MacBook warranties to give a relevant response.
Advanced Capabilities of Knowledge-Based Chatbots.
Personalization
Modern knowledge-based chatbots can provide response basing on user profile or preferences. For example, in an online store setting, the chatbot might suggest products based on the user’s previous purchases or browsing history.
Learning from Feedback
Feedback loops are essential for improving chatbot performance. Users can rete chatbot responses, allowing developers to refine the underlying knowledge base and enhance the bot’s accuracy over time.
Integration with External Systems
Knowledge-based chatbots often work best when integrated with external tools and platforms. This could include customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, or even IoT platforms.
Example: Chatbot integrated with a hospital’s appointment system can not only answer general queries about services but also help users schedule appointments in real-time.Multi-Language Support
With businesses becoming more global, the need for chatbots to support multiple languages is rising. Advanced chatbots employ machine translation techniques to process user queries in different languages and provide accurate responses without losing context.
Challenges in Building Knowledge-Based Chatbots.
Maintaining Data Freshness
Since knowledge is constantly evolving, ensuring that the knowledge base is up to date is crucial. Automated data update mechanisms are often required to keep information relevant.Handling Ambiguity
User queries can be ambiguous, and without the right context, the chatbot might provide incomplete or irrelevant answers. Contextual awareness and disambiguation techniques are key to overcoming this.Scalability
As the volume of data grows, maintaining fast and accurate responses becomes a challenge. Efficient indexing and retrieval mechanisms, such as using vector databases and semantic search, are essential for scalability.Security and Privacy
Knowledge-based chatbots often deal with sensitive information. Ensuring data privacy through encryption and secure data handling protocols is critical.
Use Cases of Knowledge-Based Chatbots
Healthcare
In healthcare, chatbots can assist patients by providing information about symptoms, medications, or appointment scheduling. They can also support medical professionals by offering quick access to medical guidelines.Customer Support
Many companies use knowledge-based chatbots to handle customer queries and reduce the load on human support agents. These chatbots can provide instant responses to frequently asked questions, improving customer satisfaction.E-Learning
Educational institutions and online learning platforms leverage chatbots to provide course information, answer student queries, and even offer study tips.Retail and E-Commerce
In the retail sector, chatbots help users find products, check prices, and even place orders. They can also provide after-sales support by answering questions about warranties, returns, or product care.For example: Ghala a product from Neurotech Africa, is a WhatsApp chatbot which enables business to sell direct through WhatsApp, it handles product search, orders placement, product payment and more.
Finance
Financial institutions use chatbots to provide customers with information about account balances, recent transactions, and financial products. Some chatbots even offer budgeting advice and investment suggestions.
Conclusion
Knowledge-based chatbots have become a vital tool in various industries, enabling instant access to information and streamlining user interactions. Unlike general conversational AI, these chatbots excel at providing precise, context-aware answers sourced from structured knowledge repositories. As technology continues to evolve, the capabilities of knowledge-based chatbots will expand further, offering more personalized, proactive, and multilingual support.
However, building and maintaining these chatbots come with challenges such as keeping the knowledge base updated, ensuring security, and handling scalability. By addressing these challenges and leveraging advanced technologies like machine learning, natural language understanding, and semantic search, developers can create highly effective knowledge-based chatbot solutions.
In an era where information drives decision-making, knowledge-based chatbots serve as an indispensable bridge between users and the vast repositories of knowledge they seek to access.
Next Step: Building a Knowledge-Based Chatbot Using Sarufi
Now that we understand what knowledge-based chatbots are and how they work, it's time to take the next step: building one using Sarufi! Sarufi provides an intuitive platform for creating advanced chatbots that can tap into structured knowledge sources and deliver highly relevant answers.
Stay tuned for detailed tutorials and real-world examples in our next posts!