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Semantic University



Semantic technologies don’t refer to a single technology, but rather to a side variety of tools and technologies that have to do with meaning. Some focus on structure, some on text, and some on intelligence. Understanding what sub-categories are out there can help you determine when to use each.

Semantic Web vs. Semantic Technologies


That Semantic Web technologies and semantic technologiesboth start with semantic is often a source of confusion.

This short lesson clarifies the relationship between Semantic Web technologies and semantic technologies.

Today’s Lesson

se-man-tic (adj): Of or relating to meaning in language.

We’ll start by defining what exactly we mean by semantic technologies and Semantic Web technologies. Then we’ll explore how they relate to one another.

Semantic Technologies

The term semantic technologies represents a fairly diverse family of technologies that have been in existence for a long time and seek to help derive meaning from information. Some examples of semantic technologies include natural language processing (NLP), data mining, artificial intelligence (AI), category tagging, and semantic search.

You might think of the goal of semantic technologies as separating signal from noise. Some examples of existing semantic technologies being used today include:

  • Natural-language processing (NLP). NLP technologies attempt to process unstructured text content and extract the names, dates, organizations, events, etc. that are talked about within the text.
  • Data mining. Data mining technologies employ pattern-matching algorithms to tease out trends and correlations within large sets of data. Data mining can be used, for example, to identify suspicious and potentially fraudulent trading behavior in large databases of financial transactions.
  • Artificial intelligence or expert systems. AI or expert systems technologies use elaborate reasoning models to answer complex questions automatically. These systems often include machine-learning algorithms that can improve the system’s decision-making capabilities over time.
  • Classification. Classification technologies use heuristics and rules to tag data with categories to help with searching and with analyzing information.
  • Semantic search. Semantic search technologies allow people to locate information by concept instead of by keyword or key phrase. With semantic search, people can easily distinguish between searching for John F. Kennedy, the airport, and John F. Kennedy, the president.

Many other modern technologies can be called semantic technologies. While all of these technologies have an overall goal in common—helping to make sense of large or complex sets of data without being supplied with any preordained knowledge about the data—they do not share much more than that. They are implemented using many different programming languages, produce data (signal) in many different formats, rely on very different underlying formalisms, and rarely work well together without investing a significant amount of effort in integration engineering.

Semantic Web

Semantic Web technologies—no matter what exact name is being used to refer to them—are a family of very specific technology standards from the World Wide Web Consortium (W3C) that are designed to describe and relate data on the Web and inside enterprises. These standards include:

  • a flexible data model (RDF),
  • schema and ontology languages for describing concepts and relationships (RDFS and OWL),
  • a query language (SPARQL),
  • a rules language (RIF),
  • a language for marking up data inside Web pages (RDFa),
  • and more.

Working Together

So what precisely is the relationship between semantic technologies and Semantic Web technologies?

In short:

  • Semantic Web technologies are a set of technologies that happen to be especially well-suited for implementing semantic technology algorithms and solutions.
  • Collectively, Semantic Web technologies are a toolbox; as such, they can be used to implement a wide variety of algorithms, solutions, and applications. However, they are particularly appropriate for implementing semantic technologies. Consider the following examples:
  • Classifying data can be accomplished very effectively by describing information using the schema and ontology languages that are part of the Semantic Web technology set.
  • Semantic search requires a way to describe data conceptually and a way to search via these concepts. The Semantic Web technology stack satisfies both these conditions.
  • NLP tools can identify unanticipated relationships between entities in source documents. The flexible graph-based data model that is one of the core Semantic Web standards is an ideal way of capturing all information obtained by NLP technology without the need to discard any data.


Semantic technologies are algorithms and solutions that bring structure and meaning to information. Semantic Web technologies specifically are those that adhere to a specific set of W3C open technology standards that are designed to simplify the implementation of not only semantic technology solutions, but other kinds of solutions as well.