“We live in a world where there is more and more information, and less and less meaning.” – Jean Baudrillard, Simulacra and Simulation

The art and science of taxonomy, developed by Carl Linnaeus, is a product of the Age of Enlightenment. From its outset, taxonomy has sought to neatly classify the world into named categories typically represented in hierarchical relation to each other. There is an essential human need to establish order in a chaotic universe, and the rooting of the world into scientific categories and nomenclature acts as a filing system superimposed on reality.

These foundations of taxonomic thinking proves to be both its promise and its challenge in a world which is now arguably Postmodern and dialectically opposed to the Enlightenment views of linear progress. Modern taxonomists and ontologists are still using Enlightenment tactics outside the scientific realm to provide meaning and order to information even as meaning and order collapse in practice. We have seen the flattening of truth as social media has provided platforms for democratized information, much of the content intentionally and unintentionally blurring truth and contextualized facts, beliefs, and baseless conspiracy theories. The fight to maintain (or establish) truth and order is at the heart of taxonomy, ontology, and semantic data work which has become increasingly in demand while also more challenging to establish in the shifting sands of truth.

Context

Ideally, taxonomies define a single, preferred concept in its one best hierarchical location. Such a concept is identified by its unique IRI (Internationalized Resource Identifier) allowing the concept to change preferred and alternative labels as necessary and add or subtract attribute properties and relationships. The foundational structure allows for both continuity and permanence and flexible change. A concept gains as much, if not more, meaning from its contextual structure as it does from its label. 

Technology functionality permitting, a concept can then be used in different contexts for different use cases without necessarily requiring the display of parenthetical qualifiers. For instance, in a navigational context, “mercury” might be displayed under “planets” and “metals” with the only difference being capitalization. On the back end, the seemingly single concept is unambiguously identified as separate concepts which could be represented as “Mercury (planet)” and “mercury (metal)”. Using semantic standards like SKOS and RDF provide frameworks for representation for both human and machine understanding. These frameworks are the underpinnings for the Semantic Web.

Despite decades of work trying to establish the Semantic Web as the norm, how the vast amount of information on the Internet is used in practice for purposes like building large language models (LLMs) does not necessarily have to retain these semantic practices. Hence, both humans and machines can potentially misunderstand labels in different contexts if those concepts are divorced from their structures. Removing context can remove meaning.

On a larger scale, removing data and ideas from their context has the same result. While it may be easy for reasonable, educated people to dismiss nonsense, cleverly constructed conspiracy theories can be built out of decontextualized information blending facts, believable or established fictions, and belief. Conspiracy theories are promulgated by bad actors attempting to spread misinformation. The rapid growth and exponential improvement of artificial intelligence has made this even easier because factual gaps can be filled with generated text, images, and video. While information scientists may work to apply metadata to such content, this metadata is not typically visible, or perhaps even believable, for the average user. Truth as intended becomes reused but revised, a cut up pastiche claiming to mirror the original but actually undermining it.

We could presumably trace the death of source of truth documents to the transition from printed to electronic documents, but there are too many examples of charlatan print works aimed to deceive, either maliciously or for entertainment. No, the shift away from truth isn’t a shift in medium, it is a shift in paradigm in which truth is derived from the context, or lack of context, in which it is presented.

Belief & Complexity

Postmodernism (or, rather, the loose and varied set of practices identified as falling under the Postmodern umbrella) has flattened our perspectives of hierarchical truth power structures and destroyed the notion of objectivity. Everything is subjective, everything is belief. No longer can we argue science versus religion, fact versus fiction, right versus wrong. In some ways, the allowance for multiple perspectives has democratized a globalized world; in other ways, it has made it nearly impossible to declare semantic truths in a world absent of absolutes.

It is no longer enough to provide evidentiary truth in opposition to supposition and unfounded belief. Conspiracy theories in particular are too interesting, too elaborate, too fascinating to crumble under the hard light of truth. At the heart of much of belief is, ironically, complexity. Belief often stems from the need to simplify an overcomplicated world operating on sometimes unknown (at least to the believer) principles beyond explanation. Good and evil, right and wrong, a New World Order, to paraphrase George W. Bush. Dialectic oppositions like us and them, right and wrong, Heaven and Hell, black and white, and so on, exist to simplify and understand a world full of grey, somewhere between dialectical opposites. Again, ironically, these easily adopted dialectics are also easily supported and reified by the adopting mind by a concoction of contextual complexity aimed at creating new truths.

Perhaps, then, the popular rise of misinformation is mirrored in the increasingly complex models used by business organizations to represent their domains. When I started in this field, most organizations outside of complex domains like biopharmaceuticals and the like were content with hierarchical taxonomies. Now, more and more of these corporations require complex taxonomies and ontologies, especially to support machine learning use cases. The complexity of semantic models mirror the complexity of the world, and, therefore, can easily mirror the complexity of truths and untruths.

Semantics

These paradigms seem to spell doom for those who aspire to create truth using semantic models and technologies. If we are in the midst of a Postmodern paradigm in which truth with a capital “T” does not exist, can semantics continue to exist as a practice? The phrase “paradigm shift” exists for a reason…or, perhaps more apropos, “this too shall pass”. As people become increasingly unmoored from meaning and the sands continue to shift under their feet, I believe that eventually they will seek some rope to pull themselves out of the quicksand. We, as taxonomists and ontologists, are here to weave rope.

As semantic professionals, we must go back to our sources, cite them, and be sure they are visible and referenceable by those who adopt the semantic models we create. We must continue to argue for the use of carefully curated semantic models as sources of truth for machine learning training data. We must continue to hire talented researchers adept at seeking and modeling truth based on semantic rather than causal relationships. We must aim to create the most truthful semantic models we can, domain by domain, regardless of whether they are reused or adopted by other companies or consumers.

If we give up on truths, regardless of whether or not they are capital “T” truths, we give in to the bad actors and malevolent forces who expect us to swallow whole the truths as they manufacture them.