Ontology Capabilities#

Semantic Reasoning#

One of the most powerful features of ontologies is their ability to enable reasoning. Reasoning allows machines to infer new knowledge from existing data. For example, if an ontology defines that all salads are food and Caesar salads are a type of salad, a reasoning system can conclude that Caesar salad is food—even if this specific relationship is not explicitly stated in the data.

This inferencing capability is crucial for solving complex problems and answering questions that go beyond surface-level information. Reasoning in ontologies is supported by formal logic and rules embedded within frameworks like OWL. These rules allow machines to:

  • Classify Data: Automatically categorize new items based on their properties and relationships.

  • Check Consistency: Ensure that data does not contain logical contradictions (e.g., an object cannot simultaneously be defined as “hot” and “cold”).

  • Discover Relationships: Identify hidden connections between concepts, enhancing data integration and discovery.

For instance, reasoning systems are used in healthcare to determine potential drug interactions based on patient data and medical ontologies. Similarly, in battery science, reasoning can help identify optimal materials by linking experimental results with material properties and performance metrics.

Thought Experiment: The One-Armed Person

Imagine we want to describe humans in an ontology. We might include some relationships like:

  • A human is a mammal

  • A human has one brain

  • A human has two arms

  • A man is a human

  • A woman is human

  • etc.

We might then create an individual called Jesper and make the statement that ‘Jesper is a man’. Reasoning according to the knowledge in the ontology will return that because ‘Jesper is a man’ and ‘a man is a human’, then it follows that ‘Jesper is a human’.

But then Jesper loses an arm in some terrible accident. We append the statement ‘Jesper has one arm’. Reasoning again will create a logical inconsistency because if ‘Jesper has one arm’ and ‘a human as two arms’, then it follows that Jesper cannot be a human.

This demonstrates how tricky it can be to maintain internal logical consistency, especially for complex ontologies, and how important it is to support correct reasoning and inferrence.

By enabling machines to “think” beyond the explicit data provided, reasoning transforms ontologies into dynamic tools for problem-solving and decision-making.

Semantic Querying#

One of the most transformative applications of ontologies is their support for semantic querying. Unlike traditional keyword-based searches, semantic queries leverage the structured knowledge within ontologies to deliver precise and meaningful results. By understanding the relationships between concepts, machines can interpret queries in a way that mimics human reasoning.

For example, if an ontology knows that “Golden Retriever” is a type of “dog,” and a “dog” is an “animal”, then a query for “animal” can return results that include Golden Retrievers, even if they were not explicitly mentioned.

Semantic querying is powered by languages such as SPARQL, a query language designed for RDF-based data. SPARQL allows users to retrieve and manipulate structured data by specifying patterns to match within the ontology’s graph. For example, a SPARQL allows users to transform natural human queries like, “Which restaurants serve vegetarian salads?” or “What materials are compatible with a specific battery type?” into a graph pattern to retrieve compatible answers. The ontology’s structure enables the system to traverse relationships and return accurate answers based on the data.

This capability is invaluable in fields like healthcare, where semantic queries can identify drug interactions or link patient symptoms to potential diagnoses. Similarly, in environmental research, semantic querying can connect datasets on climate patterns, biodiversity, and conservation efforts. By enabling machines to “understand” data, ontologies transform how we access and utilize information, making semantic querying a cornerstone of modern knowledge systems.