Introduction
In a data rich world, full of regulatory and compliance rules, organising and understanding the data owned is crucial. Ontology models help in this endeavour by creating formal structures that can be easily governed by different Subject Matter Experts. In this short blog post we will delve into the concept of ontologies and how they are used in practice.
Definition of Ontology
An ontology provides a formal and structural way of organising and representing knowledge within a particular domain. It is a systematic framework that allows the explicit definition of concepts, their attributes and the relationships between them. Furthermore, ontologies can be enriched with a set of restrictions, rules and axioms that enables a real-world representation. In information and computer science, ontology models are analogous to relational database schemas; however, the main difference is that ontologies introduce a sharable, reusable and machine-understandable way of communicating and understanding complex information consistently.
The two key components of an ontology are:
- Concepts/Classes– the building blocks of an ontology, e.g Person, Company
- Attributes/Properties– the characteristics and relationships between concepts, e.g age, knows ,is Located In.
Ontology models are the basis of knowledge graphs–collection of entities defined by specific concepts (e.g John Doe is a Person).The data within the knowledge graph now corresponds and respects the domain model axioms, restrictions and rules, whilst becoming semantically rich and machine understandable. Ontologies uplift data into knowledge.
Ontologies in Practice
Ontologies have a central role in data integration, information retrieval and AI. Their structured knowledge facilitates semantic interoperability and makes data more accessible, resulting in a reduction of costs and improvement in knowledge management across enterprises and organisations in the delivery of complex projects.
Ontologies are widely used in different verticals, such as healthcare to standardise medical terminology, and in banking whereby enterprise ontologies can be used to support data strategies and ensure regulatory compliance.
Ontologies will play a significant role in facilitating innovative and smarter AI systems. More specifically ontologies, through knowledge graphs can enhance LLMs (a) by providing dynamic and retrieval-augmented knowledge inferences; (b)interpretability for probing and analysis; and (c) pre-training by providing interoperable semantically meaningful models as LLM inputs.
Additional Resource
- Web Ontology Language (https://www.w3.org/OWL/)
- Resource Description Framework (https://www.w3.org/RDF/)
Final Remarks
Ontologies are the keystone for knowledge representation and have an immense impact on how data is organised, governed, structured and most importantly made sense out of.As you might embark (or already embarked) on a journey to the semantic world, feel free to contact us to ensure you are on the right path.
