Web, is tangled. Web, is complex. Though it started with a simple definition and a few elemental components, eventually led to explosion of standards, protocols, formats, technologies, applications etc where today defining it has become a challenge. It has engorged to an extent that it gives a hinge to stay connected and inflate to the direction of interest. Every researcher, developer, programmer, web designer, user or anyone connected to web define with their own perception. However, in spite of all the advancements, as pointed out by W3C, we yet don’t have a web of data instead it is largely controlled by applications.
Today, we talk about semantics. We talk about the data representation and operations. We have defined formal methods to verify, validate and put the data into operational use. We talk about knowledge representation in various exchange and interoperable formats. The languages or protocols for sharing meaning, for establishing foundation for semantic interoperability has been one of the key challenges in semantic web.
Web is being crawled for data. It is being mined for valuable and hidden information. Extracting valid links for the required data collection has always been a challenge. The web data explosion has led to data tools and certifications for handling petabytes and larger sized data.
Web is learning. Machine leaning algorithms have been applied to make web intelligent. Web is also mined for intelligence and predicting the future. Theories have been established long ago correlating human problem solving and machine problem solving. Just like we do, machine solving is no magic process.
Web services interact with each other providing the services as demanded by the user or other applications. Web services communicate with each other and get the data of need searching for similar services. Services have been formalized and semantics have put in a great deal. Services with static and dynamic behaviors have been evaluated on the basis of several criterion like quality of service, compliance, expressivity, scalability, robustness, dynamism, heterogeneity and autonomy.
As data became friends with science and engineering, application domain is being benefited with trendy applications. Recommendations are hinting the users with what could be their next purchase. Predictions are hints to producers on what to recommend. Though not the only area, recommendations and predictions have been theoretically proved and practically achieved. E-commerce has been greatly benefited with the beholding theory. However, there is still a lot of openness in the field. The science is never enough. Web still has the unsolved challenge to learn what information is rote and what information is meaningful.
Semantics, relevancy, recommendation, predictions, similarity, dissimilarity, representation change, abstractions, protocols, communications, service discovery, compatibility, interoperability and many other related practices have been researched to bring the related data together in order to provide a unified perspective. With all the conceptual working models around, today’s e-commerce sites still provide recommendations that are not appreciated by the user. Most of the times the recommendations are merely based on the components that go together with item of purchase or based on other user habits that did similar purchases.
With growing needs, We do need smarter recommendations. We have all the required data. We need better recommendations and the way to use the existing data.
 “W3C Semantic Web FAQ”. W3.org. N.p., 2016. Web. 21 Oct. 2016.
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