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Ontology is the theory of being. It asks the fundamental questions: What exists? What is real? Philosophers have been trying to work out the answer to these questions since at least the ancient Greeks first posed them. Ontology has been understood by philosophers as being singular. Ontology, if one were to be devised, would delineate all things that exist in the universe and their relationships with one another. From a philosophical perspective, there can be only one Ontology.

Things changed in the twentieth century. Scientists, in particular a guy named Quine, began to conceive of Ontology a theory of a specific domain. Since there were many domains and types of domain knowledge, it stood to reason that there were more than one ontology. The concept of multiple ontologies, each being a way of explaining the knowledge of a different domain, caught on among scientists. And ever since, scientists and philosophers have been trying to devise ontologies for their respective domains of research. They are trying to capture not only the things that comprise the domain, but also the concepts and processes each domain deems pertinent.

Information and computer scientists have devoted a lot of time and resources to developing ontologies. The impetus behind such research is the promise of interoperability and the sharing of data and information between information systems. The problem with computers is that they don’t grasp meaning. They can manipulate data and identify patterns of information, but they can’t create meaning for themselves or for humans. Ontologies are seen to be the keys to the kingdom, as it were, for the creation and sharing of meaning among the bits and bytes of data and information we have floating around in our information systems.

Researchers in information systems have been working on developing ontologies for a while. And they’ve failed miserably. There have only been a few successful ontologies developed, but they’re not generalizable. Why have ontology engineers and researchers failed? For many reasons, I think. First among them is their failure to understand what an ontology is–a taxonomy, a concept model, a concept map is not an ontology. Yet, time and again, researchers attempt to create rigid hierarchical structures that they think are ontologies. Second, they give primacy to their own biased worldview. They are convince that their scientific paradigms–to the exclusion of other paradigms, scientific or other–are the only valid or meaningful ways of understanding the world. They attempt to reduce the concepts and entities of a given domain to some form of quantitative measure–the only “real” measure of what exists in the world.

In combining the first two mistakes, they commit a third: neglecting schematic forms of cognition in favor of a symbolic processing paradigm. Their attempts to make ontologies interoperable amount to vocabulary matching strategies. Anyone who has ever done any language translation understands that there is often a lot of cultural filling-in that must happen in order to make the translation meaningful. There is no such thing as word-for-word translation. Yet, we keep searching for strategies that allow our machines to do just that.

Ontologies are not taxonomies. Ontologies are not intelligible as discreet quanta of information. And ontologies are not the result of symbolic processes. Ontologies are concepts, variable and schematic in nature. I also contend that they are emergent and akin to cultural schemas. How we can develop ontologies for information systems that reflect their emergent and adaptable nature is the focus of my research. I’ll have lots more to post about my theoretical perspectives and research. I just wanted to get the ball rolling.