Knowledge Graphs – The Smart Connection of Data
In companies, data volumes are growing rapidly, and quick access to relevant information can offer a decisive competitive advantage. Knowledge graphs structure and semantically link data, making connections easier to recognize and representing knowledge explicitly as a network of relationships. This makes them a foundation for explainable artificial intelligence and traceable results. Knowledge graphs unfold their full potential especially in combination with chatbots, speech recognition and speech synthesis, as they process complex content in an understandable way and deliver precise answers. In this post, we take a closer look at the benefits and use cases of knowledge graphs.
Table of Contents
What Are Knowledge Graphs?
A knowledge graph represents a network of real-world entities — such as objects, events, situations, or concepts — and illustrates the relationships between them. These entities (nodes) and their relationships (edges) are typically stored in a graph database and visualized as a graph structure. By linking entities together, relationships can be better identified and information processed more efficiently.
A knowledge graph consists of several central components:
Entities: These represent real or abstract objects such as products, people, or concepts.
Relationships: These establish connections between entities, for example "The rotor is part of the PI fan."
Properties: These describe entities through attributes, such as weight.
To model this information, ontologies and standardized data formats such as RDF (Resource Description Framework) and OWL (Web Ontology Language) are commonly used. These standards enable a uniform, machine-readable representation and facilitate the semantic linking of knowledge.
Ontologies
An ontology is a formal representation and description of knowledge. Ontologies define the structure and semantics of the data they contain. They specify which concepts, categories, and entities exist, and how these relate to one another. By using ontologies, the knowledge in a knowledge graph is organized in a formal and machine-readable way. An ontology thus serves as the foundation for the creation, extension, and use of a knowledge graph.
Applications
Knowledge graphs have numerous applications, including:
- Search engines: Google, Bing, and other search engines use knowledge graphs to make search results more relevant and precise.
- Knowledge management: Companies use them to organize internal knowledge and make it quickly accessible.
- Artificial intelligence: They serve as the foundation for intelligent assistants and chatbots, which can deliver better answers through semantic linking.
- Recommendation systems: Streaming services use knowledge graphs to generate personalized content suggestions and improve the user experience.
Knowledge Graphs, Metadata, and Chatbots
Knowledge graphs and metadata play a decisive role in optimizing chatbots. By enriching content with metadata, chatbots can deliver more precise and relevant answers, and content can be optimally prepared for chatbot use.
A knowledge graph makes it possible to store metadata in a formalized language and identify relationships between different concepts. In addition, hierarchical structures can be formed through the graph, allowing the chatbot to fall back on more general concepts if a specific piece of information cannot be found.
Together, knowledge graphs and metadata offer a powerful method for improving search and supplying chatbots with precise, contextually relevant data.
Knowledge Graphs in plusmeta
In plusmeta, our knowledge graph establishes the complex relationships between metadata and makes them visible. Based on the knowledge generated in this way, the AI classifies content with metadata and visually shows why the metadata was assigned.
The deep insight into the connections makes it clear which information is important and how it can be meaningfully linked. This allows our AI to also assign metadata for which there is no direct reference in the text.
Benefits of Knowledge Graphs
Knowledge graphs offer numerous advantages, particularly in the organization, structuring, and use of metadata.
- Transparency: Knowledge graphs help map the logic behind metadata assignment in a transparent and traceable way.
- Standardization: Existing metadata can easily be mapped to standards such as iiRDS and VDI 2770 via knowledge graphs.
- Classification: Knowledge graphs enable the assignment of metadata for which there are no direct references in the text — especially for small topics and fragments with little text content.
- Structure: Knowledge graphs show the depth and specificity of data relationships — providing clear orientation.
- Efficiency: Quickly identify how metadata can be used optimally to improve processes.
Solution: Use a digital twin to document changes during retrofits
Digital twins are an effective way of documenting all the changes that are made during a retrofit. New manuals and amended service documents can be centrally organized and easily updated using a digital information twin—as can spare parts catalogs for retrofitted machines.
Quanos InfoTwin is the answer for all machine manufacturers who want their machine information to be seamlessly interconnected and are looking for a transparent way of mapping their retrofit projects. This solution intelligently links service and spare parts information with the technical documentation, so that any changes made to equipment, machines, and systems are available to the user at all times. This makes work easier for both the manufacturer's and the operator's technicians and lays the groundwork for setting up other solutions such as aftersales portals.
Curious to Learn More?
Would you like to find out more about the many ways in which the plusmeta knowledge graph can support your content preparation? Then contact us today and arrange a no-obligation demo appointment with us. We would be happy to personally introduce you to plusmeta and its many possible applications.
