Ontology and Knowledge Graph

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An ontology that describes the concepts in the domain and also the relationships that hold the between the concept. For example, in the university domain: Professor, Student, Course etc are concepts and relationship between those concepts like a professor teaches a course and a student is enrolled in a course can be described. 

According to the Hitzler, P. (2021):

“In a semantic we context, ontologies are a main vehicle data integration, sharing, and discovery, and a driving idea that ontologies themselves should be reusable by others.”

Ontology is about things not strings.” google announced it 2012 about the their search engine strategies, meaning that they will search about things like people, places, movies and concepts etc not the traditional web links where words are matched. The search results will be drawn out from applied ontology which is known and knowledge graph.

Using the ontology gives us the context of the whole domain, by modeling the domain in a machine and human readable format.

From Ontology to Knowledge Graph?

Ontology is the foundation of knowledge Graph. Knowledge Graph is the applied ontology, which connects the ontology to the data. Without the data ontology is just a structure. 

Knowledge graph of different entity in an organization can connect to give a better representation, visualization, Querying and reasoning.

Why ontology and knowledge graph are important for AI systems?

Ontological modelling can help the AI system by broadening the scope. It can include any kinds of data type and can support unstructured, semi-structured, structured data format. Also it enables smoother data integration. It can help improving data quality for training dataset and inferences.

Ontology Editor

is a free open-source ontology editor and framework for building intelligent system developed by the Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine.


Example in Github.







Hitzler, P. (2021). A review of the semantic web field. Communications of the ACM, 64(2), 76–83. https://doi.org/10.1145/3397512