Why Your Chatbot Should Be Based On Knowledge Graphs!

Symbolic versus Non-Symbolic AI

  • Non-Symbolic AI: Learning or training an algorithm/the AI on the basis of examples or training data from which rules are derived, basically like training on the job.
  • Symbolic AI: AI learns from models and rules, so no training data is required, it’s like going to school.

Non-symbolic AI: development of machine learning-based chatbots

“Machine Learning” is only a single method of Artificial Intelligence

How to develop machine learning-based chatbots

  • You collect A LOT of sample dialogues from the field of application of the chatbot or voice assistant e.g. customer service or IT helpdesk.
  • The Chatbot is asked by users about certain topics or criteria. A human behind it, i.e. “the trainer”, decides whether the chatbot’s answer is correct or not.
  • This feedback goes to the Conversational AI system. On the basis of this “right/wrong” assignment, the machine learns which answers are correct and should be used in the future.

So when is it a good idea to use a machine learning-based chatbot?

ATTENTION!!! Machine Learning ≠ “Self-learning”

Disadvantages and limitations of a machine learning-based approach

  • Companies need a huge amount of training data to be able to map the numerous use cases that exist in reality.
  • In reality, companies are confronted with a multitude of completely different variations and question combinations for similar use cases.
  • You have to iteratively train the chatbot on an ongoing basis by interpreting the results and improving its knowledge.
  • The training approach is highly specific; even slightly different requests can usually no longer be recognised and understood.

Symbolic AI: Chatbots based on a Knowledge Graph

Knowledge Graph example — Wolfgang Amadeus Mozart

  • WAM was a composer.
  • WAM lived in Salzburg.
  • Salzburg is a city in Austria.
  • The river Salzach flows through Salzburg.

Knowledge Graphs can be used in a variety of ways after development

What are the concrete advantages of a Knowledge Graph-based chatbot?

Disadvantages/limitations of Knowledge Graph-based chatbots

Example: Development of a tourism chatbot based on the Knowledge Graph or Machine Learning approach

Some groundwork on Knowledge Graph-based Conversational AI outperforms machine learning-based Conversational AI

Medium-sized companies benefit from this in many ways:

  • Operational support from the first request
  • Minimal optimisation effort during operation
  • A large number of heterogeneous inquiries can be answered
  • Better customer experience through faster responses
  • Immediate cost savings through increased efficiency in customer service

Onlim’s approach: Combined use of Symbolic and Non-Symbolic AI

Case study: Knowledge Graph-based chatbot impresses from the beginning!

Conclusion

To remember:

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