Chatbot with knowledge graph
“Covid Q&A”

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?

Understanding texts with Natural Language Understanding (NLU)

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.

Access information and prepare dialogs with Dialog Management (DM) and Knowledge Graph (KG)

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.

Generate texts with Natural Language Generation (NLG)

Text synthesis is the counterpart to text comprehension. Here, text is generated automatically, which can afterwards be transformed into acoustic speech signals.

“Knowledge graphs that integrate different data sources,
form the basis for many AI applications and assistants.”
Prof. Dr. rer. nat Jens Lehmann
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS

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 there new cases in Mexico?”
  • “How many cases were there in total in Germany until 25th October 2020?”
  • “How many new cases were found in Argentina on 10th November 2020?”
  • “Which country had the highest number of cases on 8th November 2020?”

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”.

Contact us

Roman Teucher

Research Engineer

Fraunhofer IAIS
Zwickauer Str. 46
01069 Dresden

Phone +49 351 85477961

E-Mail senden

Image recognition and understanding

Intelligent document analysis “recognAIze”

With the intelligent document analysis of recognAIze, data from documents can be recognized and evaluated automatically.

Where does the AI application offer the greatest benefit?

The manual review of receipts, invoices and other documents, their digital capture and provision is associated with a high expenditure of time and money in many companies as well as administrative institutions. The solution is provided by intelligent document analysis systems based on optical character recognition (OCR), which, like “recognAIze”, enable fast, simple and automated analysis as well as blind processing of all types of documents. Thanks to artificial intelligence, documents are automatically captured, read, assigned and further processed. Damaged originals, low-quality scans of documents and, in particular, confidential documents are processed without further human intervention and according to high data protection standards.

What are the quality indicators in these types of AI applications?

  • The basis of document analysis is the input data that needs to be analyzed. Since the documents are usually captured in varying image quality, automated image enhancement is very important in the AI application.
  • AI-based optical character recognition (OCR) using artificial neural networks ensures that not only individual text characters are recognized and processed, but also text passages and the structure of a document (e.g. headers or footnotes).
  • Through layout analysis, the AI application can also identify tables within a document and interpret the contents to process invoices automatically in accounting, for example.
  • Particularly in the case of sensitive information, the AI used must be secure and all data must be processed in a DSGVO-compliant manner on German servers or on-premise at the customer’s premises.
  • In the future, handwriting recognition (ICR) will also play a role in the applications in order to open up additional fields of application and achieve a complete transfer of content.

What AI technology powers demonstrator of KI.NRW?

Deep Learning OCR

Optical Character Recognition (OCR) combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), two current approaches in the field of Artificial Intelligence, to develop characters from pixels. It extracts the text from the images and generates a structured XML file for each document with position data of the recognized words and page ranges.

Image enhancement

For the best possible quality of the results, negative influencing factors such as insufficient exposure of the scanned document or curvature or distortion in the image must be equalized. The image enhancement algorithms perform grayscale conversion and binarization for this purpose. In addition, procedures are used to remove curvature and other disturbing factors.

Layout recognition

The layout recognition identifies the structure of text and helps to divide the recognized characters into columns, text sections or headings and to determine a reading order. In this way, table structures can also be recognized and output again as such, e.g. as a csv file. The output format is provided with appropriate metadata.

“Thanks to the methods used for image enhancement, layout and
character recognition, even poor quality documents can be evaluated.”
Dr. Nicolas Flores-Herr
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS

What does the AI demonstrator show?

The KI.NRW demonstrator “recognAIze” brings AI-supported document analysis to life. Via the application, your own photographed or scanned documents can be uploaded to the system, where they are available for testing the intelligent document analysis. The step by step animations guide you through the AI technologies used in the demonstrator.

Optimize documents

Often, photographed or scanned documents have a fluctuating image quality, and are sometimes bumpy, torn or dirty. Image enhancement processes ensure that even old or damaged documents can be processed. The “recognAIze” demonstrator vividly guides you through the range of optimization options that are essential for high-quality document analysis.

Recognize characters and structures

The accuracy and speed of the OCR engine for intelligent character recognition from “recognAIze” is higher than that of leading market players. Without templates and manual post-processing, the demonstrator recognizes document layouts, e.g. sender information or dates. Even complex text content such as text-around-image elements are reliably recognized by the application.

Understand tables

Tables pose a particular challenge because they can be structured differently from document to document. AI methods are responsible for subdividing table contents according to information types and interpreting the segments individually.

Classify content

The demonstrator “recognAIze” determines the properties of the document, evaluates the individual elements and thereby enables a whole range of subsequent processing. For example, the intelligent classification makes blind processing of confidential documents possible in the first place. This means that information can be aggregated or used pseudonymously without a human being having access to the documents. In this way, sensitive, personal data can be protected better.

Create interfaces

AI-supported document analysis is often at the beginning of a process chain, whether in accounting or in archives. To enable further processing steps, the KI.NRW demonstrator offers various output formats such as XML or PDF.

Are you curious?
Click here to go to the demonstrator!

Where is more information to be found?

Study by KI.NRW

Learn about where we encounter modern language technologies in our everyday and professional lives and the economic opportunities (only available in German language).

AI products “Made in NRW”

Filter our AI map by “language and text comprehension”.

AI provider from NRW

Our AI map illustrates who offers AI methods related to image recognition in their portfolio.

Contact us

Dr. Nicolas Flores-Herr

Business Unit Manager Document Analytics

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 142532

Email schreiben

Dr. Iuliu Konya

Senior Research Engineer

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Phone +49 2241 142543

Email schreiben


Marius Nißlmüller

Student assistant Business Development

Fraunhofer IAIS
Schloss Birlinghoven
53757 Sankt Augustin

Email schreiben

To the top