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Semantic technology underpins conversational AI, other big data uses

Unsung and unheralded, semantic technology is a key component in artificial intelligence and other big data applications. Yet, like AI, it still faces hurdles to going mainstream.

This article can also be found in the Premium Editorial Download: Business Information: Managing the big data ecosystem requires agility amid disruptions:

After a long hibernation, artificial intelligence has awoken and seems energized to finally prove its value to businesses. One of the components underlying AI's resurgence is semantic technology, which helps users understand text, speech and relationships between data elements.

And it isn't just AI -- semantic methodologies also support a variety of other applications in big data environments.

The buzz: Like AI, semantic technology has hovered on the fringe of mainstream IT consciousness for years. It first came to life in 2001 under the banner of the Semantic Web, a concept based on the Resource Description Framework (RDF), which structures data in graph form. RDF has become a staple of semantic computing, along with the SPARQL query language and the Web Ontology Language. Now, these and other semantic tools are finding new footing in applications that parse speech, categorize questions and analyze sentiment. Uses include natural language processing, social networking, customer and healthcare analytics, and AI undertakings from Amazon's Alexa to IBM's Watson.

Example of a semantic graph data model
Semantic graphs store data as triples -- a subject and an object linked by a predicate denoting their relationship.

The reality: Enthusiasm for semantic technology could be dashed by conversational AI pratfalls on the part of chatbots and voice assistants. More broadly, it's hard to find programmers who are prepared to grapple with semantic-oriented systems. Also, semantic applications often depend on complex deployments of data lakes incorporating graph databases. And their ultimate success hinges on another AI-related technology -- deep learning algorithms that must process huge volumes of data to fuel semantic engines. With those challenges, the semantic vision may end up being a pipe dream.

Next Steps

See what a health system is doing with a semantic data lake

Learn how graph databases uncover key data relationships

Read up on potential enterprise uses of semantic technology

This was last published in October 2017

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How do you plan to exploit semantic tools in your organization?
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The graph visualisation displayed was taken from the graph visualisation software Linkurious Enterprise. There should be a watermark that has been removed. Please give credit when it's due.
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Hi, Margot. The graphic came to us without the watermark, perhaps because it's a cutout that only shows a portion of the full application screen. But we've added a reference to the visualization being created with the Linkurious software to the source line that appears when you expand the image.
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