An ontology is a formal description of knowledge in terms of a set of concepts in a particular domain and the relations that are held between them. To enable such an explanation, we have to explicitly define the components like persons (instances of things) as well as classes, attributes, and relationships as well as limitations rules, axioms, and rules. Ontologies provide not just a reusable and shareable knowledge representation but be a source of new information about the subject.
The ontology model of data can apply to any collection of individual facts to construct the knowledge graph, which is an entity collection, which is characterized by their types, and relations between them are described by edges and nodes of these nodes. By providing the structure of information in a specific domain, ontology creates the foundation so that the knowledge graph can record the information within it.
There are other ways to use formal specifications for the representation of knowledge like vocabularies, thesaurus, taxonomies topics maps, and conceptual models. But, unlike taxonomies and schemas for relational databases, for instance, ontologies define relationships and allow users to connect various concepts with other concepts in many ways.
In addition to being one of the fundamental elements of Semantic Technology Ontologies form an integral part of the W3C standards stack that is used for the Semantic Web. They give users the structure needed to connect an information piece to other information in the Web of Linked Data.Since they define common representations of data from heterogeneous and distributed databases and systems, ontologies allow database interoperability search across databases and seamless knowledge management.
One of the main attributes of ontologies is that they provide a common understanding of data, and they explicitly state domain assumptions. This means that the interconnectivity and interoperability of the model make it an ideal tool for addressing the difficulties that arise from accessing or querying information within large enterprises. Additionally, by improving the metadata and the quality of data, thereby making it easier for organizations to make understand the data they have, ontologies increase the quality of data.
One of the most important features of ontologies is because they have the fundamental connections between concepts integrated into them, they can be used to automate the analysis of data. This reasoning is easy to integrate into databases that use semantic graphs which use the ontologies for their schemas of semantics.
Ontologies also are like brains. They "work and reason" using notions and relations in ways like how we perceive interconnected concepts.
Alongside the reasoning function Ontologies also provide more coherence and easier navigation when users go from one concept to another within the structure of the ontology.
Another advantage is that ontologies are simple to expand as concepts and relationships can be easily added to existing ontologies. This means that the model can grow with the amount of data, without affecting dependent processes or systems if there is a problem or needs to be modified.
Ontologies are also able to represent all data formats that include semi-structured, unstructured, or structured data, making data integration easier as well as easier conceptual and text mining and analytics driven by data.