Extracting data with AI – Automatic information extraction with the MDM Booster

Extracting data with AI helps companies automatically gain relevant information from unstructured texts. The MDM Booster combines semantic text understanding with the identification, classification, and extraction of people, organizations, places, and other keywords. This allows data to be structured efficiently, avoids manual copy-and-paste tasks, and makes content quickly usable for further business processes.

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Named Entity Recognition (NER) and semantic text understanding

Named Entity Recognition (NER) is an AI technology that automatically recognizes and classifies specific terms in a text. These include personal names, places, companies, or dates. Within the framework of  AI-supported data extraction NER forms the basis for extracting structured information from unstructured texts.

Semantic text understanding goes further and analyzes not only individual terms, but also their meaning within context. This allows AI to understand connections and relationships between words and to intelligently interpret texts and extract data.

Example:
A system reads the sentence “Apple was founded in 1976 by Steve Jobs.”

  • NER recognizes: “Apple” (company), “1976” (date), “Steve Jobs” (person)
  • Semantic understanding also recognizes: “Apple” is not a fruit, but a company, and “Steve Jobs” is the founder.

Practical example: Automated analysis of customer feedback – efficiently extracting data with AI-supported text understanding

An e-commerce company receives hundreds of customer reviews and support requests every day via email, chat and social media. These contain valuable information about customer satisfaction, product quality and potential problems. To extract this data in a targeted manner, the company relies on semantic text understanding with artificial intelligence.

Challenge: Extracting relevant data from unstructured feedback

  • Customer inquiries are available in unstructured form (e-mails, reviews, support tickets).
  • Manual analysis of texts is relatively time-consuming and inefficient.
  • A lack of structure makes it difficult to identify trends and common problems, as relevant data cannot be efficiently extracted.

Solution: AI-supported Named Entity Recognition (NER) and semantic text comprehension

The company uses an AI solution that automatically recognizes relevant terms and their meaning in context in order to extract data in a targeted manner.

This is how it works:

  1. NER identifies important terms such as product name, customer name, date of purchase, reason for complaint or error description
  2. Information is transferred to the fields of the ticket or CRM system

Result: Automated data extraction and optimized support processes

✔ Relief for experts – Information is automatically recognized, data extracted, and provided to the support team in a structured format
✔ Information homogenization – Typos and variations are standardized, enabling easier categorization
✔ Cross-language processing – The AI ​​model can be quickly and easily trained for a few well-known terms or specialized knowledge to extract data even more precisely

Thanks to automated text analysis with AI, the company can increase customer satisfaction, better identify trends and optimize its products and services.

Typical examples of semantic text comprehension and Named Entity Recognition (NER)

The MDM Booster easily recognizes duplicates within one or a multitude of data sources from different systems.

Field of application: customer service, product management, marketing

  • Automated processing of emails, chat histories and reviews to identify customer satisfaction and common problems.
  • Automatic topic analysis (e.g. “product error”, “shipping delay”).
  • Extraction of relevant entities such as product names, customer locations or service times.

Example:
An email containing “My iPhone 13 is defective and the warranty has expired” is automatically analyzed:

  • NER recognizes: “iPhone 13” as a product, “warranty” as a contract reference
  • Semantic understanding recognizes: This fault message is not a warranty case

Field of application: Compliance, legal departments, contract management

  • Automatic recognition of parties, deadlines, contractual objects and payment terms.
  • Semantic analysis for assessing risks and obligations.
  • Comparison of similar clauses in contracts to check consistency.

Example:
A company processes a large number of maintenance contracts. An AI system should identify which customers require maintenance and whether the conditions have been met in the event of a warranty claim.

Field of application: media, market analysis, reputation management

  • Recognition of people, companies, places and events in news sources.
  • Semantic linking to recognize correlations and trends.
  • Automatic categorization of topics for targeted reporting.

Example:
A news portal analyzes hundreds of articles daily. The AI ​​automatically detects when a company is associated with a controversy and alerts the editorial team.

Area of application: Healthcare, pharmaceuticals, research

  • Extraction of diagnoses, medications, treatment methods and study results.
  • Automatic categorization of medical reports according to symptoms and diseases.
  • Support for automated patient file analysis.

Example:
A hospital scans medical reports. The AI ​​automatically recognizes the terms “Type 2 diabetes” and “Metformin” and assigns them to the fields within the patient information system.

Area of application: Marketing, market research, trend analyses

  • Identification of companies, brands and influencers in social media posts.
  • Sentiment analysis to identify positive and negative trends.
  • Automatic classification of topics into product categories or market segments.

Example:
A company wants to know how customers feel about a new product line. The AI ​​analyzes social media posts and identifies which products, events, and companies are being discussed.

Field of application: Purchasing, logistics, e-commerce

  • Automatic extraction of product information from product images, such as volume, product name or ingredients
  • Extraction of product information from product descriptions within Excel files provided by suppliers
  • Identification and extraction of information from public sources, such as websites, regulations or databases

Example:
An e-commerce company uses AI to automatically extract brand names, technical specifications, and categories from product descriptions.

Main features of NER and semantic text comprehension

Automated information extraction

The MDM Booster automatically identifies and extracts important data such as addresses, organizations, dimensions, delivery dates or invoice numbers from emails and documents.

Real-time assignment

Using individually trained AI models, data is automatically assigned – even for specialized knowledge such as abbreviations, structural formulas or rare languages.

Confidence

The MDM Booster AI solution provides you with information on reliability (confidence) for each individual field that has been extracted. Users can use the confidence to decide quickly and easily whether a case-by-case check or automated further processing should take place.  

Multilingual processing: The MDM Booster AI platform allows AI models to be trained efficiently, easily and quickly for a variety of languages.

MDM Booster 

Automated text analysis with artificial intelligence

The MDM Booster enables automated and precise analysis of texts in order to reliably identify and extract key terms, entities and correlations. Using artificial intelligence (AI), the system recognizes relevant terms for a variety of application areas, such as customer service, contract management or master data management.

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Seamless integration and customized models

Thanks to open standards and a variety of interfaces (APIs), the MDM Booster can be easily integrated into existing MDM, ERP, PIM or CRM systems. Standard formats such as SQL, CSV, Excel, OpenAPI and S3 are supported, so that smooth further processing across system boundaries is possible without any problems.

Individual AI models for customized text recognition
The MDM Booster enables the training of individual AI models that are specially tailored to company-specific requirements – without any AI expertise. This enables experts from the product and process area to train their own AI models and drive innovation in your company.

With the AI-supported text analysis of the MDM Booster, companies save time, increase data quality and use their text data efficiently for optimized processes.

Use cases for NER and semantic text comprehension

Support tickets

In the case of support tickets, emails or customer inquiries, semantic text understanding can help to precisely understand and categorize requests and optimally pre-structure the information for the experts.

Contracts & legal documents

Accurate interpretation of content is particularly important for legally relevant documents. Use the MDM Booster to train individual AI models for the automated recognition and extraction of contract terms, deadlines, disclaimers or contact persons.

Customer service

Customer inquiries are often in written form. Use the MDM Booster AI solution to automatically process tickets or inquiries and generate suitable suggestions for subsequent actions.

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Get to know the MDM Booster in the context of semantic text comprehension and recognition of proper names. MDM Booster provides companies with powerful AI software that can be used to automatically process texts, extract information and optimally implement functions such as semantic text comprehension.  

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