Ontology
An ontology is a formal representation of concepts or entities in a domain and the relationships, properties, and constraints that define them.
Knowledge Graphs and LLMs in Action
An ontology is a formal model of concepts, relationships, and constraints in a domain that supports shared interpretation and inference.
- How it is used
- Aligns heterogeneous domain data into a shared knowledge-graph vocabulary for integration, querying, reasoning, and entity disambiguation.
- How deep it goes
- Defines semantic relationships and constraints, then applies them through mappings, graph ingestion, hierarchical propagation, and reasoning.
- Examples
- Uses HPO disease–phenotype annotations, SNOMED hierarchies, and Zika entity candidates to demonstrate mapping, provenance, propagation, and disambiguation.
- What you can do after
- Build it from scratch by loading an ontology, mapping source data, ingesting graph entities and relationships, and querying the resulting knowledge graph.
Read how Knowledge Graphs and LLMs in Action explains ontology →
Digital Twins in Action
An ontology is a formal specification of concepts, relationships, and constraints in a domain that gives data a shared semantic interpretation.
- How it is used
- Gives digital-twin data shared semantic meaning so people and machines can interpret physical assets and their relationships.
- How deep it goes
- Defines classes, properties, relationships, and rules, then shows DTDL modeling and selection tradeoffs; does not develop a full ontology from scratch.
- Examples
- Uses a factory’s machines and products, a DTDL room interface, and a room sensor’s contextualized temperature reading.
- What you can do after
- Apply it via DTDL to define twin interfaces and choose whether to adopt, extend, or create a domain ontology.
AI-Powered Search
An ontology is a formal representation of concepts in a domain and the relationships and constraints among them.
- How it is used
- Structures domain relationships so search and natural-language systems can interpret concepts beyond isolated keywords.
- How deep it goes
- Defines ontology and positions it among modeling layers, but stops short of implementation or query examples.
- Examples
- Uses type hierarchies such as human is mammal and entity relationships such as John eats food.
- What you can do after
- Evaluate how an ontology fits within a domain-specific search knowledge model.
Related concepts
- knowledge graphconfigures · A knowledge graph is a graph-structured representation of entities and their semantically meaningful relationships, often including associated attributes or facts.
- RDFused by · RDF, or Resource Description Framework, is a W3C standard data model for representing information about resources and their relationships as subject-predicate-object triples.
- SNOMEDincludes · SNOMED CT is a comprehensive standardized clinical terminology for representing healthcare concepts and their relationships.
- edgehas part · An edge is a graph connection between two vertices that represents a relationship and may be directed, labeled, or weighted.
- linter rulehas part · A linter rule is a configurable condition applied by a linter to detect or enforce specified patterns or constraints in code or other artifacts.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.