What is Ontology in AI
An ontology is a basic term of knowledge as a collection of ideas within an area and their connections. Classes, individuals, characteristics, and relations, as well as rules, limitations, and axioms, must all be explicitly specified for such a description to be possible. As a result, ontologies not only provide a reusable and sharable knowledge representation, but they may also contribute new domain information.
The ontology data model may be applied to a set of individual facts to form a knowledge graph, which is a collection of things whose kinds and connections are represented by nodes and edges connecting them.
The ontology sets the scene for the knowledge graph to capture the data in a domain by specifying the structure of the knowledge in that domain.
Other approaches for knowledge representation that involve formal requirements include taxonomies, topic maps, logical models, and vocabularies. Ontologies, on the other hand, differ from taxonomies and relational database schemas in that they represent connections and allow users to relate numerous ideas to other concepts in several ways.
Ontologies are part of the W3C standards stack for the Semantic Web as one of the building components of Semantic Technology. They provide users the structure they need to connect one piece of data to other pieces of data on the Web of Linked Data. Ontologies facilitate database interoperability, cross-database search, and seamless knowledge management by specifying common modeling representations of data from remote and heterogeneous systems and databases.
Ontologies include several important properties, including ensuring a consistent understanding of information and making clear domain assumptions. As a direct consequence, the model’s interconnection and interoperability make it ideal for tackling the problems of data access and querying in huge companies. Ontologies also improve data quality by increasing metadata and provenance, allowing companies to make greater sense of their data.
Advantages and disadvantages of using ontologies
One of the most important characteristics of ontologies is that they enable automated data reasoning by including the crucial links between concepts. In semantic graph databases that employ ontologies as their semantic schemata, such reasoning is simple to implement.
- Ontologies work like a brain. They use concepts and relationships to reason in ways that are similar to how humans see interconnected notions.
Ontologies enable more cohesive and more straightforward navigation when users go from one idea to another in the ontology framework, in addition to the reasoning capability.
Ontologies are also straightforward to extend since connections and concept matching may be easily added to existing ontologies. As a consequence, if something goes wrong or has to be altered, this model develops with the expansion of data without affecting dependent processes and systems.
Ontologies also enable better data integration and data-driven analytics by representing any data type, including unstructured, semi-structured, and structured data.
While ontologies offer a diverse range of tools for data modeling, their usefulness has several drawbacks. The available property structures are one such constraint.
Another problem stems from OWL’s use of restrictions. They are used to indicate how data should be formatted and to prohibit data from being added that does not comply with these restrictions. This, however, isn’t necessarily a good thing. Data imported from a new source into the RDF triplestore is frequently structurally incompatible with OWL restrictions. As a result, before being combined with what is currently loaded in the triplestore, this new data would have to be updated.
Ontologies are frameworks for describing shareable and reusable knowledge across domains, in a nutshell. They are the foundation for modeling high-quality, linked, and coherent data because of their capacity to represent relationships and high interconnectivity.