The Web Ontology Language (OWL) is used for semantic applications, providing a framework for creating models of real-world scenarios. OWL ontologies can be instantiated, combined, and enriched using SWRL rules and reasoners like Hermit or Pellet. The data obtained from these ontologies is stored in a knowledge base and can be retrieved using SPARQL queries. However, triple stores have limitations such as high disk space consumption and slow query performance for large datasets. In contrast, Neo4j stores whole graphs, providing an easy-to-use query language, a web-based interface, fast querying, and good scalability. The choice between OWL and Neo4j depends on the specific use case, including factors like data volume, frequency of updates, and query complexity. An example use case is the BonFIRE project, which uses PROV-O ontology to analyze experiment data and track causation and influence between activities, agents, and entities. The process of importing an OWL ontology into Neo4j involves transforming data into triples, adding them to the ontology, and then querying the resulting graph using Cypher queries.