Core Ontology Framework

Updated 7 September 2025

Core ontology frameworks are rigorously defined sets of domain-independent concepts, relationships, and axioms used for structured knowledge representation.
They employ layered architectures and formal logic to bridge foundational ontologies with specialized domain models, ensuring consistency and interoperability.
These frameworks enable precise automated reasoning and scalable integration across scientific, industrial, and information systems.

A core ontology framework is a foundational, rigorously specified set of concepts, relationships, and axioms that provides an abstract, domain-independent scaffold for knowledge representation, integration, and reasoning within and across scientific, engineering, or information systems. Core ontology frameworks serve as semantic intermediaries between foundational/top-level ontologies and more specialized domain models, ensuring consistency, extensibility, and interoperability in distributed and heterogeneous environments.

1. Definition and Semantic Scope

Core ontology frameworks define a minimal, reusable, and high-precision vocabulary for structuring knowledge. Unlike lightweight vocabularies or domain-specific schemas, a core ontology framework formalizes both the categories (classes of entities or properties) and explicit relationships/axioms that underpin multiple use cases or application areas. Domain independence is central: terms are specified so that they are not restricted to any particular vertical (e.g., commerce, healthcare, cyber) but instead capture widely reusable notions—such as “object”, “event”, “process”, “situation”, “role”, or “artifact.”

Frameworks at the core level (such as the Common Core Ontologies (Jensen et al., 27 Apr 2024), ProcessCO (Becker et al., 2021), SituationCO (Olsina et al., 2021), and the Core Data Ontology (Knowles et al., 29 Sep 2024)) typically serve as the linchpin in layered ontology architectures, enabling both upward extensibility (linkage to foundational ontologies, e.g., BFO or ThingFO) and downward specializability (refinement into domain-specific modules or instance data).

2. Structural Foundations and Layered Architecture

Core ontology frameworks arise in multilayer architectures—foundational, core, domain, and instance layers being standard (e.g., FCD-OntoArch in SituationCO/ProcessCO and IOF Core/Iof-maint integration (Hodkiewicz et al., 8 Apr 2024)). Foundational ontologies (e.g., BFO, DOLCE, ThingFO) provide the highest abstraction and formal rigor, defining primitive distinctions such as continuant/occurrent, entity/assertion, or object/process. Core ontologies inherit from these, specializing primitives into richly axiomatized, yet domain-neutral concepts.

Table: Example of a layered ontology architecture.

Layer	Examples	Role
Foundational	BFO, DOLCE, ThingFO	Philosophical primitives (continuant, occurrent)
Core	CCO, ProcessCO, Iof-maint	Domain-independent scaffolding (process, entity, state, event, role)
Domain	Aerogel ontology, HCO	Application-specific concepts (e.g., aerogel microstructure, human job codes)
Instance	Actual data records	Concrete instances tied to domain models

This organization facilitates semantic enrichment from higher (more abstract) to lower (more concrete) tiers. For example, in SituationCO, terms such as “Situation”, “Human Agent”, and “Goal” are defined at the core level, with their semantics and non-taxonomic relationships inherited or enriched from ThingFO (Olsina et al., 2021).

3. Methodological Commitments and Modeling Techniques

Core ontology frameworks employ formal logic, explicit axiomatization, and modularization. Specification is often in Description Logic (e.g., OWL DL (Hodkiewicz et al., 8 Apr 2024, Gharib et al., 2018, Horvat et al., 2014)), first-order logic, or formally defined XML/BNF schemas (Kent, 2011). Core axioms include:

Preservation of classification: If instance r {of type p}, then its components are appropriately typed.
Non-taxonomic relationships: Relationships such as “consumes”, “deals with”, “relates with”, “has role”, or “is surrounded by” are defined with clear cardinalities and type constraints.
Partitioning and composition: Concepts may be further partitioned or composed (e.g., subactivities, role aggregates, semantic facets).

These ontologies do not merely provide terminological glossaries but instantiate explicit constraints and closure properties (e.g., reflexivity, transitivity, disjointness), supporting automated reasoning and validation (e.g., through OWL DL reasoners, SPARQL queries, and verification matrices (Becker et al., 2021, Gharib et al., 2018, Hodkiewicz et al., 8 Apr 2024)).

Diagrammatic representations are frequently used to illustrate both hierarchical (class-subclass) and compositional (part-of, has-role, occurs-at) relationships. For instance, in ProcessCO, foundational relationships are mapped and validated via detailed verification matrices against ThingFO (Becker et al., 2021).

4. Integration and Interoperability

Integration is central, and most core ontology frameworks instantiate mechanisms for both vertical (with upper ontologies) and lateral (across peer modules) alignment. Integration mechanisms include:

Stereotyping/semantic enrichment: Terms at the core level are tagged with stereotypes pointing to their foundational origins (e.g., << TFO::Assertion on Particulars >>) (Becker et al., 2021).
Reuse and specialization: Core modules reuse and, where needed, refine or restrict entities from foundational and other core modules (e.g., SituationCO reuses ProcessCO/GoalCO terms (Olsina et al., 2021)).
Interoperability with external standards: Frameworks such as the Common Core Ontologies are constructed for interoperability with resources like BFO, PROV-O, and the OBO Foundry, often via formal mapping and logical alignment (e.g., equivalence and subsumption mappings, property chains (Prudhomme et al., 2 Aug 2024)).

The importance of modularity is reinforced by examples such as Iof-maint, which isolates maintenance-related terms from industrial asset management and enables domain-specific extension by importing only the required core concepts (Hodkiewicz et al., 8 Apr 2024).

5. Practical Implications and Applications

Core ontology frameworks underpin a diverse array of real-world systems, supporting:

Requirements engineering: The extended ontology in (0811.4364) incorporates beliefs, desires, intentions, and attitudes, improving expressivity for stakeholder concerns over the classic Zave and Jackson model.
Data-centric system design: Models such as the Core Data Ontology and Informatics Domain Model (Johnson et al., 1 Sep 2024, Knowles et al., 29 Sep 2024), use a quadrimodal structure (objects, events, concepts, actions) to enhance data security, provenance, and multimodal interoperability.
Scientific, engineering, and social informatics: Applications span materials science (PSPP framework (Greenberg et al., 2022)), B2C eCommerce (LB2CO (Akanbi, 2014)), STEM educational environments (IDont (Chimalakonda et al., 2018)), cyber information management (TACIO (Limbaugh et al., 26 Jul 2024)), and privacy requirements engineering (COPri (Gharib et al., 2018)). These frameworks provide reusable structures that are adaptable to varying domain requirements.
Process and situation modeling: Ontologies such as ProcessCO and SituationCO provide standardized scaffolding for modeling activities, allocations, roles, and context, enabling precise representation of dynamic systems in both organizational and scientific contexts (Olsina et al., 2021, Becker et al., 2021).

6. Evaluation, Extensibility, and Ongoing Challenges

Formal evaluation incorporates automated consistency checks (e.g., HermiT OWL reasoner), SPARQL competency queries, and matrices for verifying logical coverage and alignment (Gharib et al., 2018, Prudhomme et al., 2 Aug 2024, Becker et al., 2021). Extensibility is achieved by ensuring minimal axiomatisation at the core, allowing domain extensions to impose additional constraints as required (Jensen et al., 27 Apr 2024).

Nevertheless, core ontology frameworks face notable challenges:

Balancing expressive power with usability: Increased logical rigor (e.g., non-monotonic reasoning, justified approximations) can introduce complexity that may not be fully supported by existing languages or tool chains (0811.4364).
Stakeholder elicitation and context capture: For domains such as privacy, educational design, or requirements engineering, it is challenging to formalize subjective preferences, beliefs, and context in an operationally precise manner (Gharib et al., 2018, 0811.4364, Chimalakonda et al., 2018).
Seamless mapping and alignment: As illustrated by the PROV-O/BFO mapping (Prudhomme et al., 2 Aug 2024), even with complete schema alignment, deep semantic differences must be handled with care to ensure commuting diagrams, conservativity, and consistent inference.

7. Future Directions

Research continues toward:

Formalizing modularity and meta-ontological patterns—e.g., design patterns for stasis, aboutness, processual tracing (Jensen et al., 27 Apr 2024), and analytic faceted analysis (PSPP) (Greenberg et al., 2022).
Achieving robust scalability and cross-domain interoperability via standardized frameworks (Common Core Ontologies, IOF) and automated mapping alignment pipelines (e.g., with GitHub CI/CD, automated test suites) (Hodkiewicz et al., 8 Apr 2024, Prudhomme et al., 2 Aug 2024).
Addressing emerging semantic requirements in machine learning, multimodal AI, federated and decentralized architectures—especially the need for fine-grained, auditable data provenance, role-based access control, and semantic mediation for policy and compliance (Knowles et al., 29 Sep 2024, Gharib et al., 2018).
Refining the theoretical interface between ontology and epistemology for knowledge evolution, justification, experimental reproducibility, and automated knowledge revision (Allen, 2016).

In summary, core ontology frameworks serve as the semantic backbone of modern knowledge representation, offering a rigorously defined and extensible scaffold. Their layered architecture, formal rigor, and integrative design patterns facilitate robust interoperability, precise modeling, and scalable deployment across diverse scientific, industrial, and information domains.