What are Semantic Technologies?

Semantic technology is a modern data term that can impact all industries and markets. Simply put, semantics are the concepts and ideas underlying the meaning of words, data, and concepts. Semantic technologies utilize semantics to classify and manipulate data in powerful new ways and give them meaning. During this age of Big Data, you may be looking for new ways to capitalize on your data-gathering investments. Here are three semantic technologies that are particularly promising for the future of business.

The Semantic Web and Semantic Technology

Sometimes called Web 3.0, the semantic Web is a network of files with innovative, new kinds of organizing metadata. Traditional data tables can only allow you to make connections between rows and columns. By making an extra layer of connections and links between all the data and variables inside a database, a semantic database becomes easily readable and usable across many types of systems. According to Cambridge Semantics, semantic Web technology works especially well for organizations that collect large amounts of data from diverse sources.

As envisioned by digital pioneers like Tim Berners-Lee, semantic Web technology could ultimately transform entire industries. According to Dennis Wisnosky of the Department of Defense (DOD), the DOD already uses semantic Web technology to perform basic functions. However, the development of a worldwide semantic Web is necessary before society can fully unleash the power of semantic technology.

Though similar to the World Wide Web in use today, the future global semantic Web will contain huge amounts of data that can be easily surfed and organized thanks to machine-readable tags and descriptions. Learning about semantic Web technology can help you achieve a competitive edge over companies with disorganized, limited, haphazard data practices.

Natural Language Processing

Semantic artificial intelligence (AI) is a type of AI capable of natural language processing (NLP) thanks to machine learning. Through NLP, AI applications use deductive reasoning and inference to understand and process human language. Some of the more obvious uses for NLP include automatic translation, automatic grading of papers and automatic research.

According to Dr. Michael Garbade, NLP may eventually allow machines to quickly summarize vast amounts of text. This could enable companies to quickly make information requests to a complex database without understanding its structure or pinpoint problems in lengthy contracts. NLP could also help non-attorneys more quickly understand legal documents and statutes with complex legal language.

Fortunately, there is every reason to think that NLP will grow by leaps and bounds. More powerful computer systems will allow AI to perform NLP that is closer to human language processing. Arguably, NLP is needed for more precise analysis and manipulation of the vast databases large organizations use.

Semantic Search

The traditional search engine searches databases for keywords and relates them to one another as best as it can to provide relevant results based on user intent. The semantic search engine is a proposed search engine that will search databases for concepts and ideas, providing a more intuitive and actionable end product for search users. By focusing on the meaning of a sentence or a phrase, search engines can focus on finding more relevant results, avoiding issues with synonyms, misconceptions or even lack of accuracy or use of technical concepts. Though still in its early stages, semantic search has the potential to transform the retail industry and more in the near future.

Interested in Semantic Technologies? 

Find out more about how semantic technologies and Digital Transformation can benefit your business or organization by getting in touch with the Base22 team today.

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