There is some confusion about whether knowledge graphs are synonymous with ontologies. This confusion arises because of the interchangeable use of the term knowledge graph with the term knowledge base, used interchangeably with the term ontology. This blog examines all the elements of ontology along with knowledge graphs to understand the role fulfilled by knowledge graphs in the ontology story.
A knowledge-based system uses artificial intelligence (AI) to resolve problems and is composed of two components: an inference engine and a knowledge base. Further, ontological representations are implemented to model knowledge semantically. As a result, ontologies are used as knowledge bases in AI applications to validate semantic associations and drawing of inferences from known facts. Furthermore, ontologies comprise classes, properties, and instances.
In the case of knowledge graphs, its size has been frequently touted as a critical feature. Consequently, the dissimilarity between a knowledge graph and an ontology could be understood either as a question of quantity or extensive needs. The second understanding results in the belief that a knowledge graph is a knowledge-based system that contains a knowledge base and a reasoning engine. It corresponds to the assumption regarding the superiority and complexity of a knowledge graph over an ontology as it employs a reasoning engine to create knowledge. In addition, knowledge graphs assimilate single or multiple sources of information.
In conclusion, when all these pieces are put together it is evident that knowledge graphs incorporate ontologies in their architecture. In addition, knowledge graphs use ontologies to assimilate information, which can be subsequently used for other purposes.
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