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What Is the Semantic Web?

What Is the Semantic Web and Why Should Enterprise Publishers Care?

The Semantic Web is the knowledge graph formed by combining connected, Linked Data with intelligent content to facilitate machine understanding and processing of content, metadata, and other information objects at scale.

Engineering the Semantic Web

Content engineers are creating a more powerful and agile web of content and data by first parsing and structuring the discrete elements of content that constitute websites, such as people, events, ideas, concepts, products. These elements are then assigned a “label” describing its meaning in a standardized language. When such machine-readable descriptions are present, they can be linked to build a more robust web of data where computers can find, read, and even reason about a unit of content.

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The Workings of the Semantic Web

To understand the principle behind how the Semantic Web evolved, imagine a jukebox. This classic machine plays the song a patron selects through the push of buttons. As the jukebox contains a limited amount of recordings that must be selected manually, the web before semantic technologies were introduced worked in much the same way and had many of the same limitations. Users had to manually pull requests from limited resources: web pages, directories, documents residing on different servers, etc. Machines could not find, read, or let alone use this content.

A Brief Introduction to the Technical Standards of the Semantic Web

The Semantic Web provides a lingua franca for representing and working with data. The technological framework to bring the semantic vision to life is built on standards, developed, agreed upon, and published by the W3C. These standards aim to provide a uniform and interoperable way of representing and managing data and content so that it can be easily shared and reused across applications, enterprises and individuals.


RDF stands for Resource Description Framework and is a method for publishing and linking data. It is built around the following Web standards: XML and URL (URI) and uses the model of the “triple” — three elements (namely a subject, a predicate and an object) bound together, with the relationships between them formally described. Triples, when there are enough of them, form a rich graph of knowledge.


SPARQL stands for Protocol and RDF Query Language and is the way data stored in RDF format gets queried, retrieved and manipulated on the Semantic Web. It is SPARQL that allows us to navigate diverse databases and discover relationships between data. Those familiar with SQL will notice the differences in capabilities between the query languages.


OWL stands for the W3C Web Ontology Language. OWL is a logic-based language, designed to be interpreted by machines so that they can automatically process and reason through the information it describes.

How Do We Get Started With the Semantic Web?

The Semantic Web takes a village. We need to graph our own content sets and connect them to globally related content sets. Start locally. Start at home, in our own enterprises. Then reach out to connect to the content within related content ecosystems of customers, partners, vendors, and even competitors.

Why Should We Invest in Making Our Content Semantic?

The spread of the Semantic Web and the technologies it brings to the table puts the analytical powers of machines to work in the domains of content production, management, learning, support, media, ecommerce, scientific research, knowledge management, and publishing in general. Anywhere we express knowledge will become semantic. Content discovery and presentation on Google and Bing is only the tip of the iceberg, although SEO and SERP-placement might be reason enough. When it comes to the applications of intelligent content, semantic search and smart devices, the emerging semantic web of content and data is a massive opportunity to tap into. Careers, companies, and global innovation leaders will continue to be born on the Semantic Web.

  • Connect content silos across a huge organization
  • Leverage metadata to provide richer experiences
  • Curate and reuse content more efficiently
  • Connect internal and external content sets
  • Build towards real augmented and artificial intelligence
  • Power-up authoring experiences and workflow processes

Founder of [A] (, content engineering author & speaker. Topics: content systems, customer experience, strategy, digital transformation, AI

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