To be of any use for enterprises, ontologies have to embrace a wide range of contexts and concerns, often ill-defined for environments, rather well expounded for systems.
And now that enterprises have to compete in open, digitized, and networked environments, business and systems ontologies have to be combined into modular knowledge architectures.
Ontologies & Contexts
If open-ended business contexts and concerns are to be taken into account, the first step should be to characterize ontologies with regard to their source, justification, and the stability of their categories, e.g:
- Institutional: Regulatory authority, steady, changes subject to established procedures.
- Professional: Agreed upon between parties, steady, changes subject to accords.
- Corporate: Defined by enterprises, changes subject to internal decision-making.
- Social: Defined by usage, volatile, continuous and informal changes.
- Personal: Customary, defined by named individuals (e.g research paper).
Assuming such an external taxonomy, the next step would be to see what kind of internal (i.e enterprise architecture) ontologies can be fitted into, as it’s the case for the Zachman framework.
The Zachman’s taxonomy is built on well established concepts (Who,What,How, Where, When) applied across architecture layers for enterprise (business and organization), systems (logical structures and functionalities), and platforms (technologies). These layers can be generalized and applied uniformly across external contexts, from well-defined (e.g regulations) to fuzzy (e.g business prospects or new technologies) ones, e.g:
That “divide to conquer” strategy is to serve two purposes:
- By bridging the gap between internal and external taxonomies it significantly enhances the transparency of governance and decision-making.
- By applying the same motif (Who,What, How, Where, When) across the semantics of contexts, it opens the door to a seamless integration of all kinds of knowledge: enterprise, professional, institutional, scientific, etc.
As can be illustrated using Zachman concepts, the benefits are straightforward at enterprise architecture level (e.g procurement), due to the clarity of supporting ontologies; not so for external ones, which are by nature open and overlapping and often come with blurred semantics.
Ontologies & Concerns
A broad survey of RDF-based ontologies demonstrates how semantic overlaps and folds can be sort out using built-in differentiation between domains’ semantics on one hand, structure and processing of symbolic representations on the other hand. But such schemes are proprietary, and evidence shows their lines seldom tally, with dire consequences for interoperability: even without taking into account relationships and integrity constraints, weaving together ontologies from different sources is to be cumbersome, the costs substantial, and the outcome often reduced to a muddy maze of ambiguous semantics.
The challenge would be to generalize the principles as to set a basis for open ontologies.
Assuming that a clear line can be drawn between representation and contents semantics, with standard constructs (e.g predicate logic) used for the former, the objective would be to classify ontologies with regard to their purpose, independently of their representation.
The governance-driven taxonomy introduced above deals with contexts and consequently with coarse-grained modularity. It should be complemented by a fine-grained one to be driven by concerns, more precisely by the epistemic nature of the individual instances to be denoted. As it happens, that could also tally with the Zachman’s taxonomy:
- Thesaurus: ontologies covering terms and concepts.
- Documents: ontologies covering documents with regard to topics.
- Business: ontologies of relevant enterprise organization and business objects and activities.
- Engineering: symbolic representation of organization and business objects and activities.
Enterprises could then pick and combine templates according to domains of concern and governance. Taking an on-line insurance business for example, enterprise knowledge architecture would have to include:
- Medical thesaurus and consolidated regulations (Knowledge).
- Principles and resources associated to the web-platform (Engineering).
- Description of products (e.g vehicles) and services (e.g insurance plans) from partners (Business).
Such designs of ontologies according to the governance of contexts and the nature of concerns would significantly reduce blanket overlaps and improve the modularity and transparency of ontologies.
On a broader perspective, that policy will help to align knowledge management with EA governance by setting apart ontologies defined externally (e.g regulations), from the ones set through decision-making, strategic (e.g plate-form) or tactical (e.g partnerships).
Open Ontologies’ Benefits
Benefits from open and formatted ontologies built along an explicit distinction between the semantics of representation (aka ontology syntax) and the semantics of context can be directly identified for:
Modularity: the knowledge basis of enterprise architectures could be continuously tailored to changes in markets and corporate structures without impairing enterprise performances.
Integration: the design of ontologies with regard to the nature of targets and stability of categories could enable built-in alignment mechanisms between knowledge architectures and contexts.
Interoperability: limited overlaps and finer granularity are to greatly reduce frictions when ontologies bearing out business processes are to be combined or extended.
Reliability: formatted ontologies can be compared to typed programming languages with regard to transparency, internal consistency, and external validity.
Last but not least, such reasoned design of ontologies may open new perspectives for the collaboration between cognitive humans and pretending ones.
- Knowledge Architecture
- Ontologies & Models
- Ontologies & Enterprise Architecture
- Ontologies as Productive Assets
- Open Concepts
- Caminao & DoDAF
- Systems, Information, Knowledge
- Unified Architecture Framework Profile (UAFP): Lost in Translation ?
- Stanford Knowledge Systems Laboratory: “What is an Ontology ?”
- John F. Sowa, “Ontology“
- Zachman Framework
- Sowa John F. “Knowledge Representation: Logical, Philosophical, and Computational Foundations”, Brooks / Cole (1999).
- Sowa John F., John A. Zachman, “Extending and formalizing the framework for information systems architecture”, IBM Systems Journal, February 1992.
- James Odell, CSC Catalyst Ontology
- Semantic Web Case Studies and Use Cases
- FacetOntology Tetherless World Constellation (TWC)
- Vehicle Sales Ontology