Ontologies and AI: structuring knowledge and shaping the future
Aristotle’s quote “The whole is more than the sum of its parts” impressively describes a fundamental truth: complex systems do not develop their effect solely through their individual parts, but through the way in which these parts interact with each other. But what does this mean in the context of artificial intelligence (AI), ontology and metaphysics? And what relevance does this have for SMEs in Germany?
Metaphysics as a conceptual basis
Metaphysics deals with the fundamental structures of reality. In its sub-area, ontology, questions are asked about “being”: What exists? How are things connected to each other? Ontologies that emerge from metaphysics are also of central importance in computer science today. They help to structure knowledge and create connections between concepts.
One example of such a connection is the Semantic Web, which uses ontologies to enable semantic processing of data. This enables machines not only to store information, but also to understand and process it in a meaningful way. Ontologies therefore represent a bridge between the abstract philosophical foundations and the practical application in AI.
Karl-Heinz Land emphasized in an article in the Frankfurter Rundschau that ontologies and knowledge graphs are not only tools for efficiency and transparency, but can also help to implement ethical standards in companies. These standards are of central importance in view of the increasing pressure on companies to act responsibly and sustainably.
Why ontologies are essential for AI
In order to make the theoretical foundations of ontology effective in practice, clear structures are required that organize complex data sets in a meaningful way. Ontologies play a decisive role here, as they pave the way from unstructured data to usable knowledge. Three key aspects illustrate why ontologies are indispensable for modern AI systems.
Firstly, ontologies provide a precise representation of knowledge by defining terms and their relationships. This enables a systematic representation of complex relationships. In medicine, for example, the relationships between symptoms, diagnoses and therapies can be mapped in a structured way. This clear organization is the basis for training specialized AI applications.
Secondly, ontologies facilitate semantic understanding by enabling machines to capture context. This is particularly relevant in applications based on natural language interaction. Language models such as GPT or Perplexity benefit from an ontologically organized knowledge base, as it enables them to understand user queries more deeply and precisely.
Thirdly, ontologies allow automated reasoning, i.e. the drawing of conclusions that go beyond the directly coded information. Expert systems can thus derive new insights from known facts and support complex decision-making processes.
These three aspects illustrate how ontologies lay the foundation for powerful and context-aware AI systems. Their versatility ranges from structuring specialist knowledge to supporting strategic decisions.
Industry Competence Ontologies (ICOs) and Corporate Knowledge Graphs
A unique selling point of neuland.ai AG is the development of Industry Competence Ontologies (ICOs). These industry-specific ontologies offer standardized basic structures that enable companies to organize their domain-specific knowledge efficiently. ICOs form the basis for the creation of Corporate Knowledge Graphs (CKGs), which map a company’s individual knowledge and link it to powerful AI models.
According to Karl-Heinz Land, ICOs and CKGs not only create a competitive advantage, but also a basis for responsible corporate governance. They help to secure knowledge in the long term and promote innovation. The concrete benefits include:
- Knowledge retention: Company knowledge is retained even when personnel change.
- Process optimization: Automated analyses and recommendations improve efficiency.
- Ethics and responsibility: Through transparency, companies can ensure that their values are put into practice.
Land emphasizes that such systems not only promote efficiency, but also credibility and trust – key factors in an increasingly critical and demanding society.
Applications in practice: Semantic Web and ethics
The practical importance of ontologies is reflected in their versatility, which ranges from technological to ethical applications. One prominent example is the Semantic Web, which uses ontologies as a basic building block to link data together. Companies such as Google use ontologies in their knowledge graphs to make search results not only more relevant, but also more contextually appropriate. This makes it possible to provide users with information that is precisely tailored to their questions.
Ontology-based systems also make a key contribution in the area of knowledge management. They create transparency and accelerate decision-making processes by making connections visible. A practical example from the pharmaceutical industry shows how knowledge graphs are used to quickly identify links between active ingredients and side effects. This not only optimizes research processes, but also improves patient safety.
Ontologies also make a significant contribution to anchoring ethical standards in companies. They help to make principles such as sustainability or fairness visible and to monitor compliance with them. According to Karl-Heinz Land, ontologies create the basis for doing business in a way that is not only profitable but also responsible. At a time when consumers are placing increasing value on ethical behavior, this is becoming a decisive competitive advantage.
Integration through AI operating systems
For ontologies to be fully effective, they require a powerful technological infrastructure. Platforms such as neuland.ai’s AI operating system offer the right solutions here. These platforms are not only used for technical integration, but also provide an overview of the various AI applications and their interactions.
A central function of these platforms is the coordination of all AI applications. Uniform control allows synergies between the different systems to be optimally utilized. At the same time, the integration of data protection and compliance mechanisms ensures transparency and reduces the risks associated with the use of sensitive data.
Another advantage is scalability. Companies have the flexibility to use the operating system both locally and in the cloud, depending on their individual requirements. Thanks to comprehensive monitoring, they always retain cost control, which is particularly important for medium-sized companies.
The combination of powerful technology and sophisticated knowledge management makes it clear: without a robust infrastructure, the potential of ontologies remains untapped. The AI operating system from neuland.ai creates the conditions for using knowledge strategically and thus securing long-term competitive advantages.
Ontologies as the basis for AI operating systems: This diagram shows how companies can gain competitive advantage through technology integration, data privacy compliance, scalability, cost control and optimized application synergies.
Conclusion: Philosophy and technology in dialog
The connection between metaphysics, ontology and AI is more than just a theoretical consideration – it has practical consequences. Ontologies create a basis on which companies can use their knowledge strategically and secure competitive advantages. This is particularly relevant for German SMEs, which can act more sustainably and responsibly by using such technologies. As Aristotle’s quote suggests and Land emphasizes: The whole is more than the sum of its parts.