Chatbot with knowledge graph
The chatbot demonstrates the functionality of an online dialog system that uses the strengths of a knowledge graph.
Where does the AI application offer the greatest benefit?
A chatbot supported by AI offers a wide range of possible applications and assists companies in any area of communication with employees or customers, regardless of the industry. A classic example are customer-oriented service offerings that are made available twenty-four hours a day. Companies use them on websites, in online stores, on support pages, in apps, or via instant messaging systems to simplify navigation on websites, to answer specific customer inquiries, or to structure access to service and customer support.
However, chatbots can be used successfully not only in external but also in internal corporate communications. Application examples can be found in the onboarding of new employees, in HR or administrative processes such as questions about vacation requests or payroll tax settlements, as well as in the support of complex assembly instructions in production.
Chatbots can also be supplemented by the component of acoustic speech recognition as well as acoustic speech synthesis. In this extended form, we speak of voice assistants (voicebot), similar to Siri or Alexa.
What are the quality indicators of such AI applications?
NLU methods are based on semantic representations of texts. They can understand and map relationships between words. These semantic representations exceed the possibilities of the classical rule-based methods of text mining.
Knowledge Graphs structure data and knowledge, enable semantic linking, and in many cases are the basis for making artificial intelligence applications explainable and providing results that are comprehensible to humans.
Text synthesis is the counterpart to text comprehension. Here, text is generated automatically, which can afterwards be transformed into acoustic speech signals.
What does the demonstrator of KI.NRW show?
An AI-based dialog system (“question answering system”) shapes the search for information more efficiently and conveniently for the user. The KI.NRW demonstrator shows such an AI-supported chatbot for querying Corona case numbers worldwide. The animations explain step by step how it operates.
Step 1: Understanding natural language
By using AI, speech recognition is oriented to real conversations of humans and the so-called natural language (“natural language understanding NLU”). The models extract information from the text, which they represent internally in a way that allows further processing. This enables the system to understand less common terms, dialects or everyday language. Associations and connections between words are also taken into account appropriately, for example, that the word invoice is related in content to the word payment.
Step 2: Structure data and knowledge
Knowledge graphs structure data and knowledge, enable semantic linking and, in many cases, are the basis for shaping AI applications explainable. A knowledge graph is able to combine a wide variety of information sources into a dynamic knowledge base. In the case of the KI.NRW demonstrator, Corona case numbers from Johns Hopkins University and the Robert-Koch-Institut are included. They are made accessible with the status of the previous day via a knowledge graph.
Step 3: Generate answer
Finally, an answer is generated that matches the asked question. Thus, this process is the matching counterpart to the first step, the understanding of language. The structured data is now converted into text and output. The output as an acoustic signal can also follow here (for example with the so-called voicebots).
The AI chatbot in action
Try out the AI chatbot for yourself: As an example, the chatbot was set up on data that depicts the worldwide case numbers surrounding the Corona pandemic. Anyone who asks a corresponding question in English via the chat window will receive an immediate answer.
Test questions for the knowledge query can be the following:
Are you curious?
Click here to go to the demonstrator!
Where is more information to be found?
AI provider from NRW
Our AI map illustrates who offers AI methods related to “knowledge and inference” in their portfolio.
AI methods around knowledge and inference
Many “good practice examples” as inspiration for the possible applications of this AI.
AI products “made in NRW”
Filter our AI map by “knowledge and inference”.
Prof. Dr. rer. nat. Jens Lehmann
Lead Scientist “Knowledge Graphs & Conversational AI”
Zwickauer Str. 46
Phone +49 351 85477950
Zwickauer Str. 46
Phone +49 351 85477961
Dr. Diego Collarana Vargas
Senior Research Engineer
Zwickauer Str. 46
Phone +49 351 85477960