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Produce UX including user flows, wireframes, prototypes, storyboards, user personas, mobile apps, websites and web applications (JS experience highly desired) -

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Semantic Web

While its critics have questioned its feasibility, proponents argue that applications in industry, biology and human sciences research have already proven the validity of the original concept.[5]

A 'Semantic Web', which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines.

Each triple represents one edge in the resulting graph: the first element of the triple (the subject) is the name of the node where the edge starts, the second element (the predicate) the type of the edge, and the last and third element (the object) either the name of the node where the edge ends or a literal value (e.g.

The second graph shows the previous example, but now enriched with a few of the triples from the documents that result from dereferencing (green edge) and (blue edges).

from the document at (green edge in the Figure) allow to infer the following triple, given OWL semantics (red dashed line in the second Figure):

When applied in the context of the modern internet, it extends the network of hyperlinked human-readable web pages by inserting machine-readable metadata about pages and how they are related to each other.

These are used in various contexts, particularly those dealing with information that encompasses a limited and defined domain, and where sharing data is a common necessity, such as scientific research or data exchange among businesses.

Currently, the World Wide Web is based mainly on documents written in Hypertext Markup Language (HTML), a markup convention that is used for coding a body of text interspersed with multimedia objects such as images and interactive forms.

In the examples below, the field names 'keywords', 'description' and 'author' are assigned values such as 'computing', and 'cheap widgets for sale' and 'John Doe'.

With HTML and a tool to render it (perhaps web browser software, perhaps another user agent), one can create and present a page that lists items for sale.

The HTML of this catalog page can make simple, document-level assertions such as 'this document's title is 'Widget Superstore'', but there is no capability within the HTML itself to assert unambiguously that, for example, item number X586172 is an Acme Gizmo with a retail price of €199, or that it is a consumer product.

There is also no way to express that these pieces of information are bound together in describing a discrete item, distinct from other items perhaps listed on the page.

In this way, a machine can process knowledge itself, instead of text, using processes similar to human deductive reasoning and inference, thereby obtaining more meaningful results and helping computers to perform automated information gathering and research.

I think maybe when you've got an overlay of scalable vector graphics – everything rippling and folding and looking misty – on Web 2.0 and access to a semantic Web integrated across a huge space of data, you'll have access to an unbelievable data resource … 'Semantic Web' is sometimes used as a synonym for 'Web 3.0',[13]

Web 3.0 has started to emerge as a movement away from the centralisation of services like search, social media and chat applications that are dependent on a single organisation to function.[14]

Guardian journalist John Harris reviewed the Web 3.0 concept favorably in early‑2019 and, in particular, work by Berners‑Lee on a project called 'Solid', based around personal data stores or 'Pods', over which individuals retain control.[15]

The collection, structuring and recovery of linked data are enabled by technologies that provide a formal description of concepts, terms, and relationships within a given knowledge domain.

In a corporation, there is a closed group of users and the management is able to enforce company guidelines like the adoption of specific ontologies and use of semantic annotation.

Critics question the basic feasibility of a complete or even partial fulfillment of the Semantic Web, pointing out both difficulties in setting it up and a lack of general-purpose usefulness that prevents the required effort from being invested.

While learning the basics of HTML is relatively straightforward, learning a knowledge representation language or tool requires the author to learn about the representation's methods of abstraction and their effect on reasoning.

[…] These abstractions are taught to computer scientists generally and knowledge engineers specifically but do not match the similar natural language meaning of being a 'type of' something.

As it turns out, specialized communities and organizations for intra-company projects have tended to adopt semantic web technologies greater than peripheral and less-specialized communities.[32]

[…] cost-benefit tradeoffs can work in favor of specially-created Semantic Web metadata directed at weaving together sensible well-structured domain-specific information resources;

This phenomenon was well-known with metatags that fooled the Altavista ranking algorithm into elevating the ranking of certain Web pages: the Google indexing engine specifically looks for such attempts at manipulation.

An advanced implementation of the semantic web would make it much easier for governments to control the viewing and creation of online information, as this information would be much easier for an automated content-blocking machine to understand.

Another criticism of the semantic web is that it would be much more time-consuming to create and publish content because there would need to be two formats for one piece of data: one for human viewing and one for machines.

Another argument in defense of the feasibility of semantic web is the likely falling price of human intelligence tasks in digital labor markets, such as Amazon's Mechanical Turk.[citation needed]

Data Analyst (Part-time Internship)

We’re looking for people to join our cause – to simplify the visual web with really smart but simple visual tools.

Using R&D in machine learning and computer vision technology, ViSenze can recommend visually similar items to online shoppers, either on e-commerce platforms when they browse or search by uploading a picture or on content publishers platforms like social media and video networks.

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