An ontology primer – under the hood of AI

As AI is set to eat the world, it is important to delve into the ontology that underlies many AI systems. The ontology forms the fundamental structure upon which AI algorithms operate, enabling them to comprehend, learn and interact with the world. It is through the ontology that knowledge-based reasoning and inference in AI are established.  

In domains where intricate relationships, hierarchies, and interconnected information prevail, an ontology provides a structured framework to represent and organise this complexity. This is especially evident in fields such as healthcare, finance, and scientific research. When seamless communication and information sharing between different systems or components are necessary, an ontology serves as a common language. Within the realm of AI, the ontology serves as the foundation for organising data. It encompasses concepts, entities, relationships, and the rules that govern them. The ontology acts as a structural framework that allows the system to organise and interpret data in a meaningful way.

The UK National Health Service (NHS) is currently in the final throes of appointing an AI provider to manage one of the world’s most valuable databases. As this is topical right now, I will use a medical AI ontology to illustrate the components of AI ontology in this post. US data analytics and AI-developer Palantir Technologies assisted the NHS manage the COVID-19 pandemic and is widely expected to the awarded the next large NHS AI contract.

Key components of an AI Ontology

Concepts and Classes. At the core of AI ontology are concepts and classes, representing the fundamental building blocks of the knowledge system. These entities encapsulate various aspects of the world, ranging from tangible objects to abstract ideas. For example, in a medical AI ontology, “disease” and “treatment” could be essential concepts. A subclass of “disease” could be “Cardiovascular disease”, “Infectious disease”, or “Neurological disorder”.

Relationships. The relationships between different concepts define the connections and dependencies within the knowledge base. These connections allow AI systems to understand how various elements relate to each other. In a medical AI ontology, “Has symptom” could be a relationship, for example, “Patient X as the symptom of persistent cough”.

Properties. Properties define the attributes or characteristics associated with concepts. For example, in a medical AI ontology, properties of a “disease” could be “symptom”, “treatment” and “prevention”. 

Axioms and Rules. Axioms and rules establish the logical constraints and principles governing the relationships and properties within the ontology. They contribute to the logical structure and reasoning capabilities of the system. The rules guide the reasoning abilities of AI systems, allowing them to infer new knowledge based on existing information. An example, in the medical context: “If a patient exhibits symptoms A, B and C, and these symptoms are associated with Disease X, then there is a likelihood that the patient has Disease X”.

These concepts such as classes, relationships and properties are obviously very familiar among object-oriented software developers.

Medical applications of AI Ontology

A medical ontology can be used in a Clinical Decision Support System (CDSS), however this in just one example in the medical field. A CDSS utilising a medical ontology can significantly improve the quality of healthcare delivery by assisting clinicians in decision-making processes, diagnosis and treatment planning.

The medical ontology forms the knowledge base representing structured information about diseases, symptoms, treatments, medications, and their relationships. Classes, subclasses, relationships, and axioms in the ontology define the context and knowledge for medical knowledge.

The CDSS incorporates an inference engine that uses the ontological structure to perform reasoning and draw logical conclusions. Axioms and rules within the ontology guide the inference engine in making informed decisions and recommendations.

A user interface allows clinicians to interface with the system. Clinicians can input patient data, symptoms and diagnostic information. Decision-support algorithms leverage the ontology to analyse patient data, identify potential diagnosis, and suggest appropriate treatment. The ontology helps in considering patient-specific factors such as medical history, allergies, and risk factors.

The AI system is not only for diagnosis, but can also be used for prediction. Healthcare administrators can also use AI for streamlining administrative tasks, including appointment scheduling, billing, and managing healthcare records. This improves operational efficiency. It can also facilitate remote patient monitoring through wearable devices and sensors which collect real-time data. AI can also assist with fraud detection and security. The AI can analyse large data sets to identify trends and patterns in public health aiding in the detection of outbreaks, monitoring population health, and informing preventative measures.

Of course, an AI-powered medical ontology can provide virtual assistants and chatbots to provide information, answer queries, and offer basic healthcare advice to patients. They can help in analysing X-rays, and in triaging and directing individuals to appropriate care. Natural Language Processing (NLP) technology can help extract valuable information from unstructured healthcare data, such as clinical notes and medical literature, making it more accessible for analysis and decision-making.

Challenges and Future Directions

While ontology has proven instrumental in advancing artificial intelligence, challenges persist. The dynamic nature of information, the need for continuous updates, and the interdisciplinary nature of knowledge are areas for improvements.

The future of AI ontology holds promise in addressing these challenges, through adaptive learning, ontologies can evolve with changing information. Integrating more advanced reasoning capabilities and enhancing ontologies with a deeper understanding of context will further propel the capabilities of intelligent systems. AI ontology serves as the bedrock of artificial intelligence, enabling machines to navigate and understand the intricacies of the world. As we continue to push the boundaries of AI, a deeper exploration and refinement of ontology will be key to unlocking new realms of intelligent capabilities.

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