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Use Cases Dictate How You Adopt the Semantic Web

Designing Semantic Web applications involves a much more sophisticated and forward-thinking set of use cases than traditional web applications do. Find out why.


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he most widespread—and likely most reported on—Semantic Web technology is a W3C recommendation called RDF (Resource Description Framework). An XML-based language for representing data in knowledge bases, RDF is used in nearly all existing online knowledge bases. But while the spotlight is on RDF, other technologies such as NLP, SPARQL, ontologies, and inference all work in concert to enable the Semantic Web stack.

To explain how the Semantic Web can be useful in next-generation web development, this article provides a survey of these core Semantic Web technologies and concepts. Along with outlining the technologies, it examines their advantages and disadvantages when compared with traditional web development tools.

Entity Resolution and Access to Limitless Knowledge

Natural Language Processing (NLP) is a technology that translates human-readable language to machine-readable language and vice versa. It is not a pure Semantic Web technology because it can exist outside the Semantic Web. In fact, it can easily be applied to any traditional application. For example, NLP can determine sentiment in a block of text or important topics in a news article. So whether you are working on a product review application that determines the role of positive or negative reviews or an application that works with news articles, NLP by itself can offer a number of benefits.



This process provides unprecedented access to practically limitless knowledge.

In the Semantic Web, however, NLP's value is enhanced. When dealing with text, a Semantic Web application can leverage NLP to perform something called entity resolution, which is a process of connecting important bits in a text block to the many interconnected, freely available knowledge bases on the web. Because so many of these knowledge bases, called ontologies, are represented in the common RDF standard, they can consume and integrate any entity as soon as NLP resolves it. This process provides unprecedented access to practically limitless knowledge.

Yet sifting through these enormous sets of data is a real challenge. How is one really supposed to work with them? What are their intended uses? Here are a few examples of entity resolution in practice:

  • Biotech: An entity resolved to denote some compound can be used to get knowledge about that compound and how it works with others compounds.
  • News media: An organization can have their topics of interest resolved as entities in varying ontologies to retrieve additional facts about that entity, including its history and how it is related to other items in the news.
  • Industry domain agnostic: An application simply takes text as input to better serve and personalize its responses to the user. After resolving entities in the text, the system can fetch and gather more relevant data about those entities.



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