Artikel
How knowledge engineering is transforming the data landscape in companies
Today’s companies have more data at their disposal than ever before, giving them enormous potential. Used correctly, data enables better decisions, reduces blind spots, and reveals complex relationships. It can increase efficiency, capture customer needs more accurately, and strengthen competitiveness.
In practice, however, the opposite is often true. Many companies get lost in heterogeneous data landscapes. Valuable information remains unused because it is hidden in data silos or its quality leaves much to be desired. More data does not automatically mean more insight. Distorted, outdated, or misinterpreted information can even worsen decisions.
This is precisely where knowledge engineering comes in. It is a central component of modern knowledge management and uses artificial intelligence methods to convert data into structured, networked knowledge. This goes far beyond mere data storage. Semantic models, intelligent search, and AI-supported knowledge processing create context, enable machine understanding, and ensure transparency.
Knowledge engineer Dr. Amir Laadhar explains how companies can use knowledge modeling to identify connections, make knowledge usable, and make more informed strategic decisions.
Dr. Amir Laadhar is a senior knowledge engineer. His expertise lies in the application of semantic technologies and the development of innovative data solutions for businesses.
The challenges of modern corporate data landscapes
Many companies have huge amounts of data stored in different systems, from traditional databases to ERP and CRM systems to unstructured documents and emails. This fragmentation means that information often remains isolated, is difficult to find, and can only be used to a limited extent.
Dr. Amir Laadhar emphasizes that the problem is not only technical in nature. “Companies often have valuable knowledge that is not networked and therefore cannot be accessed in context,” he explains. The consequences are inefficient processes, repetitive work, and a limited basis for decision-making on strategic measures.
In addition, traditional search and analysis tools are increasingly reaching their limits. Information is often unstructured, semantic relationships remain hidden, and the amount of data is growing faster than it can be processed manually. Without a comprehensive approach such as knowledge management and knowledge engineering, knowledge gaps arise that impair innovation and competitiveness.
Technologies in knowledge engineering: knowledge graphs, ontologies, and AI
“Knowledge engineering primarily involves modeling knowledge using artificial intelligence methods and tools. It’s not just about storing data, but systematically capturing and structuring knowledge and making it usable for companies,” explains Dr. Amir Laadhar. “This allows relevant concepts, their relationships, and rules to be mapped in order to reveal connections and improve processes. This is the basis for modern knowledge management.”
To ensure that knowledge does not remain fragmented but becomes systematically usable, knowledge engineering makes use of a number of key technologies.
Knowledge graphs are structures that represent information as nodes and edges, thereby making connections visible. They enable context-related queries, conclusions, and flexible extensions, even with very heterogeneous data sources.
Ontologies are definitions of terms and their relationships in order to organize knowledge consistently and in a way that machines can understand. They create a common language across systems and departments and form the backbone of knowledge engineering, so to speak.
Semantic data integration links data from different sources so that it can be used in a contextually coherent manner.
AI integration enables the use of artificial intelligence, e.g., large language models, for the automated processing, contextualization, and analysis of knowledge.
Knowledge platforms bundle these technologies and make them usable in practice. Intelligent solutions such as PANTOPIX SPHERE support companies in connecting existing data and systems to create an information world and thus use their information for specific applications.
“This is only a small part of the methods we knowledge engineers use in our work,” explains Dr. Laadhar. “But they are essential. We use them not only to make information accessible, but also to ensure that applications such as intelligent search, AI-based Q&A systems, and decision support function reliably. Knowledge engineering is thus transforming the way companies deal with knowledge.”
Knowledge engineering in practice: use cases for companies
Knowledge engineering unfolds its value particularly in concrete applications that bring direct benefits to companies. Dr. Amir Laadhar explains: “Theory is important, but it is only in practice that this work improves processes and makes knowledge truly accessible.”
Here are a few examples:
Product Information Management (PIM)
“The requirements for PIM systems have changed significantly,” Dr. Laadhar points out. Today, it is no longer just about the central management of product data. Complex product structures must be represented, and different types of information must be integrated and provided in a context-dependent manner.
Traditional, purely data-based approaches to product data management can only fulfill this to a limited extent. The work of knowledge engineers offers suitable solutions for this. “By connecting to a knowledge graph, product information is not only stored but also linked to each other. Products, variants, components, documents, media, target markets, and applications are semantically related.”
We have already described how a PIM system based on knowledge graphs works in another article.
Technical documentation
Traditional approaches are also increasingly reaching their limits in technical documentation. Products are becoming more complex, variants more numerous, and regulatory requirements stricter. However, documentation must be consistent, up-to-date, and adapted to different target groups. According to Dr. Laadhar, this is where knowledge engineering has significant potential. “Knowledge-based models allow content to be structured modularly and linked semantically. Individual information modules know which product, variant, and usage context they belong to.”
Changes to a component or a regulation can thus be automatically taken into account in all relevant places. This reduces redundancies, lowers error rates, and significantly speeds up the creation and maintenance of technical documentation.
Technical documentation and knowledge graphs are an exciting combination that we have already explored in a previous presentation.
Service and support
In service and support, knowledge often determines the speed and quality of the solution. At the same time, this knowledge is often scattered across ticket systems, manuals, training documents, and the minds of experienced employees. Knowledge engineering helps to bring this fragmented knowledge together and make it usable. “A knowledge graph can link error patterns, causes, affected components, solutions, and empirical values,” explains Dr. Laadhar. “This provides service employees with context-related answers instead of isolated information. AI-supported assistance systems also benefit from this, as they are based on a sound knowledge base.” The result is shorter processing times, more consistent answers, and a noticeably better customer experience.
In addition, there are numerous other areas of application that benefit greatly from knowledge engineering. From risk management, which would be simplified by the automatic checking of guidelines and regulations, to marketing, where automated analysis of customer feedback and market information contributes to the data-driven optimization of strategies.
These examples show that knowledge engineering, as the methodological foundation of effective knowledge management, extends far beyond IT applications and delivers strategic advantages in many different areas of a company. It makes knowledge visible, usable, and measurable, making it a decisive factor for innovation and competitiveness.
What changes knowledge modeling triggers in companies
When companies want to focus more strongly on sustainable knowledge management, roles and tasks naturally change, but proven structures usually remain in place, according to Dr. Laadhar. “In our daily project work, it is very clear that external knowledge engineers complement existing teams quickly and easily. They work closely with the specialist departments and bring knowledge into the systems in a structured way.”
The topic of data quality is gaining attention. “Even basic measures such as clear terminology, linked information, and consistent documentation ensure that knowledge systems are directly usable and support decision-making.”
Collaboration becomes more efficient because knowledge is available across departments. “The goal is to break down data silos step by step without having to restructure teams.” Employees benefit immediately from more accessible knowledge, learn connections more quickly, and can contribute their expertise in a targeted manner.
Warum Knowledge Engineering ein Schlüssel für modernes Knowledge Management ist
“Knowledge engineering is a pragmatic introduction to sustainable knowledge management that helps companies systematically leverage knowledge and make more informed decisions,” summarizes Dr. Laadhar.
Even small, targeted steps bring measurable benefits to companies. Clear goals, a pragmatic approach, and the involvement of specialist departments are crucial. It is not technology alone that creates added value, but rather the combination of knowledge, structure, and direct practical relevance. Platforms for intelligent knowledge management such as PANTOPIX SPHERE support this approach by making knowledge engineering accessible in specific use cases.
“Our project experience shows that it is helpful to rely on an experienced external partner,” adds Dr. Laadhar. “Methodological know-how, proven process models, and sufficient resources ensure that pilot projects are implemented quickly without disrupting existing processes in the company. This allows initial results to be achieved quickly and developed in a targeted manner.”
Sandy Hedig
Marketing Manager
PANTOPIX
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Maraike Heim
Head of Marketing
- maraike.heim@pantopix.com
