Read about the Semantic Web and what it takes to reach its potential and evolve from a Web of Documents to a Web of Data.
Analysts reporting on the tech industry are at times ‘too clever by half’ when promoting trends or shooting down other people making yearly predictions about the ever-evolving field of data analytics. An apt example, is the considerable campaign against the Semantic Web and its benefits to enterprises and intelligence agents all over the world.
Many condemn so-called vagueness of the Semantic Web and try to push the narrative that the uncertainties, which arise in the development of large ontologies makes the concept obsolete. But the fact remains that some people either condemn the idea out of ignorance or they want to push their own agendas.
Some understanding of the Semantic Web’s concept – like, for example, to accurately extract meaning from unstructured content through the interoperability standards such as HTTP, XML, HTML, RDP and OWL – should be enough proof that integrating cognitive computing into the concept will significantly reduce the uncertainties it currently faces. Thereby providing the leverage required to take the Semantic Web to its next level.
But for some people, simple explanations aren’t enough. Therefore, there is a need to provide more in-depth analysis on how cognitive computing and semantics can work hand in hand to advance the concept of the Semantic Web.
Cognitive computing has played and continues to play a huge role in extracting implicit semantics from unstructured data and it employs the use of cognitive tools such as pattern recognition, machine learning and Natural Language Processing to achieve its victories.
It is of note that the extracted entities, relationships, analysis, sentiments and other parameters play a stellar role in developing the semantic web constructs which includes RDF ontologies needed to build annotations, tags, and metadata.
These parameters aid the creation of a consistent semantic structure from the unstructured characters in a data store. Here, cognitive computing pushes the semantic web a step further by facilitating and automating the semantic process involved in translating and connecting data to create accessible frameworks.
Cognition as a Service (CaaS) which should also be viewed as a subset of cognitive computing, has also been hailed as a viable replacement for when we give up on pursuing the idea of developing a common framework that aids data exchange on the Web. Proponents of this idea continue to argue that making every mobile app, web app and operating system intelligent enough to interact with its users will be more helpful to enterprises and individuals than the semantic web.
These proponents fail to understand that the very concept of CaaS ties into the semantic web process but from a more individualistic perspective. For each intelligent app will simply be cognitive in its own niche while shunning information it deems irrelevant. Examples are Google’s Alpha Go, Apple’s Siri and NexT IT’s Alme platform which employs cognitive computing powered by CaaS platforms developed exclusively for their parent companies.
In this light, the best case scenario for CaaS is each tech powerhouse developing its own CaaS platform and pawning the corresponding APIs to developers for profit. On the other hand, the semantic web aims at implementing semantic standards among heterogeneous environments, including CaaS ecosystems, thereby allowing content everywhere to be available, readable, searchable and comprehensible to both human and automated consumers.
The practical benefits of a collaboration between semantic technology and cognitive computing can be better understood using Welltok, a healthcare platform which uses semantic technology, as a case study.
In its aim to provide better results and programs tailored to fit its users health status, Welltok took advantage of cognitive computing by integrating IBM Watson’s cognitive abilities into its CafeWell platform. Welltok’s CafeWell which analyzes a user’s health information and provides real-time healthcare recommendations, added an extra layer of intelligence thereby building a semantic framework which provided more specialized help to patients.
This brilliant move, added an extra layer of intelligence that allows the CafeWell platform understand its users better. Now, users on the platform can receive modified recommendations as the platform learns more about their health status and needs by accessing new data.
Cognition—the machinery of rational thought—on its own is definitely empty without semantics and while the race to enhance cognitive computing and Cognition as a Service are admirable, it is worth noting that leaving the semantic web behind will be counterproductive to the big data analytics industry in the long run. So while we discuss the new age of cognitive computing, integrating its concepts into approximately 2 decades of semantic web growth must be a part of these discussions.