Key takeaways:
Knowledge graphs represent entities and their relationships.
Ontologies define concepts, relationships, and rules within a domain.
Concepts in ontologies can be primitive or fully defined.
Relations in ontologies include taxonomies and associative links.
Instances are specific entities in concepts, usually excluded in ontologies.
Axioms set rules or constraints for concepts.
Knowledge graphs focus on relationships, while ontologies standardize knowledge.
Knowledge graphs are used for querying, ontologies for AI and web applications.
A knowledge graph, also known as a semantic network, is a visual representation of interconnected real-world entities, including objects, events, situations, or concepts and their relationships. It serves as a structured way to organize and understand information by depicting how various entities are related to one another.
Typically stored in a graph database, a knowledge graph showcases entities as nodes and their relationships as edges, forming a graph structure. This setup allows for efficient querying and retrieval of information and facilitates complex analyses.
Ontologies
Ontologies are formal representations of knowledge graphs designed to capture the concepts, entities, and relationships within a specific domain. It establishes a structured framework for representing generalized data, focusing on common properties shared among objects within a domain.
For instance, “fruit” serves as a generalized model, while “apple” represents a specific entity within that category. We develop simplified and abstract views of available data by creating conceptualizations based on ontologies. The value of ontologies lies in enabling easy interpretation and action on data by both humans and machines, enabling efficient utilization of information resources.