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Contrasting Styles for the Semantic Enterprise

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Similar to different styles and patterns in software programming, there is not a single (nor best, depending on circumstance) way to approach becoming a semantic enterprise.[1]

This article contrasts two styles. The more traditional and familiar one is comprehensive, complete and “engineered” in its approach. The second, and emerging style, is more adaptive and incremental, an "open" approach. The use and applicability of either approach is really a function of objectives and circumstances. There is a gradient — or spectrum — of possible approaches between these contrasting styles. By understanding these differences it should be easier to place your own organization at the right points along this spectrum.

A Spectrum of Advantages and Differences

The general idea of semantics in the enterprise preceeds the use of the term, having been somewhat captured before by the ideas of enterprise application integration, enterprise information integration and other concepts even related to data federation and data warehousing stretching back to the 1980s. However, as a specific label, the first mentions occurred in the late 1990s, with more concerted attention beginning from about 2002 or so onward.

The early understandings were on things like automated reasoning, machine-aided decision making, aspects of artificial intelligence, and so forth. Often, the early emphasis was on “big changes” and possible disruptions.

With recent developments and successes, an alternative model is now emerging. We can now balance our understanding of what it means to be a semantic enterprise. We can contrast the characteristics of the two approaches or styles as follows:

Characteristics of the
Comprehensive, ‘Engineered’ Style
Characteristics of the
Open Style
  • A focus on a more complete, comprehensive coverage of the semantics in the domain
  • More enterprise-wide, less partial or departmental
  • Greater emphasis on “closed world” approaches; more akin to relational database architecting and schema
  • Expansion is possible, but effort may be somewhat complex
  • A general implication is to replace or supplant existing information structures with semantic ones
  • Not necessarily based on semantic Web standards and languages [2] (e.g., may include Common Logic, frame logics, etc.)
  • Richer set of predicates (relations)
  • Though a distinction is maintained between schema and instances, their separation may not be consistently (physically) enforced
  • Often more complicated inferencing and logic tests
  • More complete enumeration and characterization of items
  • Much process around semantics agreement across groups
  • Fairly well-developed implementation tools, including for ontology engineering
  • Implementation times in months to years
  • Implementation costs akin to traditional large-scale IT projects
  • An emphasis on a simpler, incremental, “learn as you go” approach
  • Start with single departments or limited vertical apps
  • Embedded in the “open world” approach, with incorporation of external information
  • Design and approach inherently allows incremental expansion and adaptation
  • A key premise is to build from and leverage existing information structures, vocabularies and assets
  • Fully based on semantic Web standards and languages [2], often including linked data
  • Tends to start simply with hierarchical or related concepts (e.g., SKOS)
  • Conscious distinction in the structure for handling schema separate from instances 
  • Inferencing logic based more on concept matching, or parent-child or part-of relationships
  • Degree of item characterization based on current scope
  • Initial semantic matching can be driven from existing assets
  • Fairly well-developed implementation tools, except for how to engage publics in the development process
  • Implementation times in weeks to months
  • Implementation costs driven by available budgets (and thus scope)

Note we have labeled the conventional approach as the “comprehensive, engineering” style; its contrast is the “open” style.

Though the table above contrasts many points, there are two main distinctions to the adaptive approach. First, it firmly embraces the open world assumption. OWA is key to an incremental, “learn as you go” deployment that is also well suited to incorporation of external information. The second main distinction is to leverage and build from existing assets.

A Spectrum of Applications

Yet as noted in the opening, which of these approaches makes better sense depends on circumstance. One aspect of circumstance is available budget and deployment times for pilots or proofs-of-concept. Another aspect, of course, is the planned use or application for the deployment.

These are by no means hard distinctions, but in general we can see these contrasting approaches applying to the following uses:

Applications and Uses for the
Comprehensive, ‘Engineered’ Style
Applications and Uses for the
Open Style
  • Bounded, “inward” applications (high degree of control and completeness)
  • Engineering enterprises
  • Technical domains and organizations
  • Aeronautics
  • Pharmaceuticals
  • Chemicals
  • Petroleum
  • Energy
  • A/E firms (construction)
  • External facing applications, organizations (customers, incorporation of external data)
  • Faceted Search
  • Taxonomy updates
  • Multi-domain master data management (MDM)
  • Simple (initially) inferencing
  • Consumer products
  • Finance
  • Health care
  • Knowledge enterprises

A critical distinction is the nature of the enterprise itself. “External-facing” enterprises or functions that want or need to incorporate much external information (say, marketing or competitive intelligence) may be better suited to the open approach. Organizations that have more complete control over their circumstances should perhaps focus on the conventional approach.

The key advantage of the adaptive, incremental "open" approach is that the whole IT game in the enterprise can change. An open world approach enables adoption as it proves itself and as budgets allow. Commitments made under this approach are robuse in the face of uncertainty. With learning, targets can be re-adjusted, structure re-defined and applications re-focused, all as new discoveries and broadening scope dictate.


  1. An early version of this article first appeared in, M.K. Bergman, 2010. “Two Contrasting Styles for the Semantic Enterprise,” AI3:::Adaptive Information blog, Feb. 15, 2010
  2. 2.0 2.1 See for example RDF, RDFS, OWL , SKOS and SPARQL and others
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