Cognitive computing can take the semantic Web to the next level

As big data analytics pushes deeper into cognitive computing, it needs to bring the semantic Web into the heart of this new age

The IT industry is approaching a point where we'll soon need to distinguish between "cognitive" and "semantic" computing. The terms are blurring into each other. Actually, the distinctions among them have never been clear. Sometimes it's even easier to refer to them both by the even vaguer "smart computing" to allude to the practical magic they enable.

I recently came across an article that promotes the concept of "cognition as a service" while stating bluntly that the "semantic Web [has] failed," as if the concepts are mutually exclusive or aiming at exactly the same objectives. For many years, I've blogged my thoughts on the "semantic Web" (as a Tim Berners-Lee initiative at W3C) vs. "semantic interoperability" (as a long-standing data-integration imperative). I won't rehash it here, other than to say semantic technologies have permeated every aspect of the enterprise architecture and cloud universe. They have most certainly not failed to take hold.

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Cognitive computing can't achieve its potential without a strong semantic-processing substrate that executes across diverse content sources. It's good to see that the above-cited article's author, Nova Spivack, references IBM Watson in this regard. The cloud service's DeepQA technology incorporates semantic approaches into its very core, balancing the use of strict and shallow semantics and leveraging many loosely formed ontologies to deliver precise answers to natural-language queries.

In my recent big data predictions for 2014, I state that cognitive computing -- much of which will move into the cloud -- incorporates and extends the innovations pioneered by the semantic Web community. Here's what I said: "In 2014, more big data, advanced analytics, and business intelligence platforms will adopt cognitive-computing capabilities to automate sense-finding, natural language processing, decision-automation, and semantic search functions against all data and media types." If you want my further perspective on cognitive computing, check out this blog from November.

You could view cognition in the cloud, per Spivack's discussion, as the next evolutionary plateau for the semantic Web. But this demands we have a shared understanding of the relationship between the concepts of "cognitive computing" and "semantic computing." The affinity is clear cut. Cognitive computing refers to the ability of automated systems to handle conscious, critical, logical, attentive, reasoning modes of thought. Semantic computing facilitates and automates the cognitive processes involved in defining, modeling, translating, transforming, and querying the deep meanings of words, phrases, and concepts.

Semantic computing is what natural-language processing, the heart of cognitive computing, is doing. Data scientists use cognitive-computing tools -- natural-language processing, pattern recognition, and machine learning -- to extract the implicit semantics from unstructured content sources. The extracted entities, relationships, facts, sentiments, and other artifacts are used to fashion the Semantic Web constructs -- such as RDF ontologies -- that drive the creation of indices, tags, annotations, and other metadata that layer a consistent semantic structure across the various items within an unstructured-data store.

Cognition, the machinery of rational thought, is empty without semantics. It would be counterproductive for the big data analytics industry to push deeper into cognitive computing without bringing the semantic Web into the heart of this new age.

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