Ontology or Living Context?

By Charles Clendenen, Infoglide Director of Professional Services

IBM’s Jeff Jonas recently wrote a post about what types of problems are appropriate for developing an ontology, i.e. “a rigorous and exhaustive organization of some knowledge domain that is usually hierarchical and contains all the relevant entities and their relations.”  His key point: as the complexity of the data that the ontology is meant to organize increases, the value of the ontology decreases.

Building on that idea, it’s equally true that the dynamic versus static nature of the data sources determines the value of the ontology. John Ripley speaks about a “living context” that continuously evolves and adapts to the data. The notion is that challenging problems arise from the reality that today’s solutions draw from dynamic and disparate data stores. e.g. departmental silos, web services, semantic web info, and public and private data sources, and that “facts” in an online, interconnected environment change constantly.

Dealing with all that complexity and change using a static ontology is not feasible for many problem domains. New relationships between entities and new attribute values for those entities require constant readjustment. Flexibility is key. Every assumption is an opportunity for errors to be introduced and compounded. Every change introduces a new piece to the puzzle, and the more rigid the ontology, the more likely it is that the new piece will not fit.

That the available data will change is a certainty. That our view of the data will become more complex is likely. That we will be able to adapt new data to fit existing technology is not at all certain. It certainly makes more sense to assume that entity resolution technology must adapt to the data, not the other way around. Change will happen, and we must be prepared to deal with it.