by Madhurjya Chowdhury
February 2, 2022
Ontologies increase data quality by enhancing information and provenance
An ontology is a concise overview of knowledge as a collection of concepts within a field and their relationships. Individuals, classes, properties, and relations, as well as constraints, rules, and axioms, must all be explicitly specified in order for such a description to be possible. As a result, ontologies not only provide a reusable and easily shared knowledge representation, but they can also add new domain information.
The ontology data model can be implemented to a list of discrete facts to generate a knowledge graph in which nodes and edges represent the types and connections between them.
Ontologies for Better Data Management
Ontologies include several important properties, including ensuring a consistent understanding of information and making explicit domain implications. As a result, the model’s interconnection and interoperability make it ideal for addressing the problems of data access and querying in huge companies. Additionally, ontologies increase data quality by enhancing information and provenance, allowing businesses to better understand their data.
The Benefits of Using Ontologies
One of the most important characteristics of ontologies is that they permit automated data reasoning by incorporating the crucial relationships between concepts. In semantic graph systems that use ontologies as their semantic conceptual frameworks, such reasoning is simple to implement.
Furthermore, ontologies work like a ‘brain.’ They use concepts and relationships to ‘work and reason’ in patterns that are similar to how humans see interconnected notions.
Ontologies enable more cohesive and easier navigation when users go from one concept to the other in the ontology framework, in contrast to the reasoning feature.
Ontologies are also straightforward to extend because relationships and concept matching can be easily added to existing ontologies. As a result, if anything goes wrong or needs to be altered, this model develops with the growth of data without affecting dependent systems and processes.
Ontologies also enable better data integration, easier idea and text mining, and data-driven analytics by allowing any data format to be represented including semi-structured, unstructured, and structured data.
Limitations of Ontologies
While ontologies provide a varied set of tools for data modeling, they have a number of disadvantages.
The available property structures are one such constraint. Although the most recent edition of the Web Ontology Language, OWL2 has a rich collection of class constructions, it has a fairly limited number of property capabilities.
Another problem stems from OWL’s use of restrictions. They’re used to specify how data should be structured and to prevent data from being added that violates these guidelines. This, however, isn’t always a good thing. Data transferred from a fresh batch 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 fresh data would have to be updated.
Ontology Use Cases
Ontologies can be used in many applications to record relationships and improve knowledge management as they define the vocabulary used to define and portray an area of information.
By categorising identified explicit linkages to a causality connection ontology, ontologies aid early hypothesis testing in the pharmaceutical industry. Semantic web mining, mining medical histories for insights, fraud prevention, and semantic publishing all benefit from ontologies.
Ontologies are frameworks for defining shareable and adaptable knowledge across domains, in a nutshell. They are the foundation for modelling high-quality, connected, and coherent data because of their capacity to describe relations and high interconnectivity.
Do the sharing thingy