Technology is the foundation for Structured Dynamics'
products and services. SD's principals, Fred Giasson and Michael Bergman, and their
blogs, are often one of the go-to sources for innovation and
insight regarding the semantic enterprise. In this brief
summary of our writings, we try to capture some of the
factors and influences at the core of our work.
Adding connections to existing information via linked data is
a powerful force multiplier, similar to Metcalfe's
Law for how the value of a network increases with more
users (nodes). We call this the Linked Data
Law: the value of a linked data network is
proportional to the square of the number of links between
data objects. If we are purposeful to include connective
links where appropriate as we add more data, this multiplier
effect becomes even stronger.
The RDF Data Model
RDF (Resource Description Framework) provides this multiplier and the common model to which any data format or schema can be converted and represented. It also provides a logic model and basis for building the vocabularies (ontologies) that can inform and drive generic tools. With a canonical data model, talking to other sources and formats (N) only requires converters to and from the canonical form (2N). Without a canonical model, the combinatorial explosion of required format converters becomes N2.
RDF has the logical basis to represent any data form and any schema or conceptual structure. It is based on a robust set of open standards and languages and tools. It may be serialized in many formats. It can be grounded in description logics and, in appropriate forms, reasoned over and expressed in vocabularies and schema suitable for the most complex of conceptual structures and semantics. RDF is the data model explicitly designed for the Web, the clear global information basis for the foreseeable future.
The canonical RDF data model and its ontologies can be
layered over existing data. Thus, existing information assets
can be left in place, with the RDF interoperability layer
added incrementally and without displacement. Because of the
hundreds of RDF converters for existing data formats --
including relational databases -- any existing data structure
can be federated. With appropriate ontologies, the schema and
semantics of all underlying systems can also be integrated.
This opens entirely new horizons for the existing practices
of enterprise information integration, master
data management (MDM) or corporate taxonomies.
Incremental and Cost-effective Methodologies
A strength of RDF and ontologies is that they can be built incrementally and can easily change. This makes ontologies a different animal from relational schema, which are notoriously brittle with expensive re-architecting required whenever scope or schema change. As a result, Structured Dynamics advocates lower cost and lower risk deployment methodologies. Ontologies can (and should!) start small. Ontologies can (and should!) grow incrementally.
Ontologies provide an organizing context for relating disparate information together and for making meaningful inferences. But the framework itself is a function of the world view, context and domain scope at hand. As a result, there is only context, and not some single, universal "truth." The trick to properly designed ontologies is to maintain internal coherence and self-consistency. As long as it is coherent, the "correct" ontology is the one that best captures the scope and domain at hand.
Ontologies are a vehicle for developing a common world view within the enterprise. Ontology development can become a means for developing and refining a common language within the enterprise through consensual or community processes. As language or conceptual understandings change, so can the vocabulary or structure of the ontology change. There is no "lock in." This flexibility results from the fact that ontologies -- properly constructed -- can drive the generic set of tools in data-driven applications. Ontologies can change without any adverse effects on the applications based on them.
Like corporate taxonomies or MDM, ontologies thus provide a
framework for enterprises to develop internally consistent
common languages or vocabularies. Unlike corporate taxonomies
or MDM, ontologies can drive directly generic tools and
Our Unique Take on Adaptive Ontologies
Ontologies can represent knowledge structures that have otherwise been around in various forms for years. For decades enterprises have created schema, taxonomies, controlled vocabularies, standards, and other knowledge structures that represent untold dollars and effort. It is a waste to not fully leverage these sunk investments. Many ontologies and interoperable structures also exist external to the enterprise, many open source and freely available, that also deserve consideration.
So, the role of adaptive ontologies is not to create new structures or new representations from scratch, but to leverage current structures. These existing structures have been hard-earned, codified over years of effort, and are legacy expressions of the enterprise's knowledge base. Structured Dynamics' methods aggressively mine and re-use existing knowledge and structure.
Thus, adaptive ontologies and their associated systems are not a replacement for existing data assets. Rather, the objective is to keep data records intact and in place as much as possible. The role of adaptive ontologies is to act as a federation layer that bridges across these existing assets. This approach is aided by the ability to convert in-place data to ontology-ready RDF form. Adaptive ontologies reconcile the semantics across the enterprise's data stores.
Applying this mindset does not require elaborate methodologies nor is it limited to some priesthood. Sure, there are some things to learn and some practices to follow, but these are relatively easy to understand and master. Adaptive ontologies done right can be a participatory activity within most any organization. The opportunity -- besides getting on with learning and gaining the benefits from this new paradigm -- is to engage all knowledge stakeholders in ontology creation, review and refinement. In this manner, users become the developers and maintainers of the knowledge systems upon which they rely.
Adaptive ontologies are fast to develop, easy to change, responsive to new knowledge and perceptions, and robust and flexible. Indeed, it is the structure and nature of these adaptive ontologies that is the heart and secret of data-driven applications. Any knowledge worker can understand and refine the organization and relationship of information via these structures. And, most importantly, the resulting ontologies are sufficient to drive the generic applications that are based on them.
This methodology is truly a new way to do business. We can now remove prior bottlenecks arising from the need to customize applications, configure report writers, or wait for IT to generate SQL queries.
We encourage you to see how these technology influences are
powering Structured Dynamics' products.