As the importance of understanding the context grows, while driven by trends like AI-readiness, interoperability, and semantic technologies, the idea of modeling at a conceptual level has again gained a fair amount of attention. However, terminology around this topic often causes confusion. In particular, the terms conceptual modeling, concept modeling, and conceptual data modeling are frequently used interchangeably, despite their distinct meanings and historical roots. I’ll clarify these three concepts and highlight where the confusion tends to arise.
Conceptual modeling
Conceptual modeling is the broadest of the three concepts. It refers to the high-level abstraction of systems or knowledge domains, representing key entities, relationships, rules, or behaviors regardless of whether the focus is on data, processes, software, or business logic.
To expand the notion, consider:
- A floor plan as a conceptual model of a building,
- A musical stave as a conceptual representation of a melody, or
- A chemical structure diagram as a conceptual model of a compound.
Conceptual modeling serves as a cognitive tool to help us understand, reason about, and communicate complex systems. It acts as a “roof term” that can encompass both conceptual data modeling and concept modeling, depending on the modeling intent and domain.
Concept modeling (terminological concept modeling)
Concept modeling, also known as terminological concept modeling, is grounded in terminology science and is primarily concerned with defining, classifying, and relating concepts within a specific domain. It is often employed in ontology engineering, semantic systems, and terminological databases to ensure semantic clarity across systems or languages.
Conceptual data modeling
Conceptual data modeling emerged from the field of database design, with its roots in Peter Chen’s 1976 paper introducing the Entity-Relationship (ER) modeling. It remains a cornerstone of data architecture, serving as the conceptual design layer of databases: defining entities, attributes, and relationships that reflect real-world structures without concern for physical implementation.
However, this term has occasionally been applied to broader modeling disciplines outside of databases, which contributes to semantic ambiguity. This is particularly problematic when better-fitting terminology already exists for such broader conceptualisation.
Summary of differences
To summarize, conceptual modeling is the broad practice of abstracting and representing systems or knowledge at a high level. Within that, concept modeling deals with structuring domain-specific meanings and terminology, while conceptual data modeling focuses on capturing data structures conceptually for database design. Understanding these distinctions is critical for avoiding cross-disciplinary miscommunication, especially as data, AI, and semantics increasingly converge.
Conceptual modeling is a meta-level activity that includes both concept modeling and conceptual data modeling, but isn’t limited to them.
- Focus area: General abstraction across domains
- Domain roots: Systems engineering, architecture
- Primary purpose: High-level conceptual representation of systems or ideas
Concept modeling captures domain semantics and terminology, often for ontology development.
- Focus area: Terminology, concepts, ontology
- Domain roots: Terminology science, linguistics
- Primary purpose: Representation of domain knowledge, meaning, and relationships
Conceptual data modeling is tightly linked to database design and structure.
- Focus area: Data structures, entities, databases
- Domain roots: Database design (ER model, UML)
- Primary purpose: Conceptual representation of database schemas
As a final verdict, we shouldn’t try to model business logic with conceptual data modeling. This is seen way too often and it usually ends up bringing in the worst of both worlds. So, let’s be clear on what we are modeling and choose our tools according to that.
