Ontology Abstraction

For Ontologies to serve as a mechanism for reconciling diverse systems and as an integration engine, they must be flexible and separable from the rules (and the systems applying those rules) that would define the nature of the interactions. This is what is referred to as "Ontology Abstraction."

Ontologies are also separate and abstracted from meta-data. One reason that most data standardization efforts have either failed or seriously underperformed is the idea that anyone can fully define the nature of all enterprise data and predict how that will evolve over time - it always has been and will remain wholly unrealistic.

What we need instead are less painful ways to capture and characterize the changing nature of our enterprise data environment. Using Ontologies for this will allow us to manage data in human readable formats that can readily be shared with end users as well as functional experts. Those groups will define Ontologies based upon generic long term expectations (formal sets), dynamic evolution and discovery (dynamic sets) and the business logic logic needed to manage both (interaction rules).



One of the key concepts underpinning Semantic Integration is Ontology Abstraction

Copyright 2008, Semantech Inc.

Context Mapping

What if in the same organization a word such as, let’s say “SOA,” meant one thing to the majorityof stakeholders (our best criteria ought to be some sort of official or community endorsement) on May 20th, 2006and then meant something slightly different on November 11th, 2006 and then something completely different on July 10th, 2007? Which meaning is valid? In most cases, we’d simply go with the latest version of the meaning. But life is never that simple is it? It just so happens that we’ve been given the charter to integrate with four other organizations who all have various interpretations of the same word that all have evolved over time. How can we reconcile this; or even study it?

Now you begin to see the value of Context and Dynamic Context. Without the ability to view the variations andtheir respective evolutionary paths it will be difficult for us to determine an appropriate, Integrated reconciliation for that term – one that will be accepted by all the constituents of all groups (or most of them anyway). Context Mapping is the ability to visualize the evolution or comparison of Vocabulary terms, Taxonomies, Ontologies or Sets within or across constituent perspectives, i.e. Contexts. Once a reconciliation has been chosen it can be used to generate a Dynamic Set or Sets. Of course, part of the reconciliation process for Context Mapping could include generating “What If” Dynamic Sets to see how these would perform against Semantic Rules and Formal Sets. This process would be used to determine what types of logic and data will be used to integrate organizations across domains, it will determine the structure of all systems or systems involved as well as the processes which link them.

Put another way, Context Mapping represents one of two core analytical processes involving Contexts; Mapping supports the reconciliation of both Context and Dynamic Contexts into “Integrated Contexts.” Integrated Contexts are the building blocks for Dynamic Sets and Semantic Rules.

Copyright 2008, Semantech Inc.

Context & Dynamic Context

Semantics must be able to support analysis as well as application interoperability.Context represents our most powerful analytic mechanism. Generically, Context refers to the specific perspective ofany group, individual or entity in regards to any combination of Semantic information. Dynamic Context takes this astep further by combining any given Context with a unique point in time. The reason for this is clear, Context is not a permanent state; perspectives change with time. The only way to accurately determine true Context is to capture it in its relative state. Context can also be tracked across time to illustrate the evolution of any given perspective.

The reason that most data standardization efforts have failed in IT is that unless one is working in a highly controlled environment, differing Contexts cannot be accommodated. Ultimately, someone or some group always feels left out and in fact they are. When dealing with information across hundreds, thousands or even millions of users or organizations, traditional data standardization methods and techniques can never hope reconcile the differences and support interoperability on a global scale. If Context or Dynamic Context is understood we can then determine how to construct Dynamic Sets that allow us to Interact with Shared Formal Sets. The future of all integration may very well become centered around the creation of Dynamic Sets and Dynamic Semantic Rules. These tools will determine the exact information (structured and unstructured) and logic we may need to accomplish any given task, answer any given question or solve any given problem.

Copyright 2008, Semantech Inc.