Artificial intelligence gets a lot of press these days. However, it is easy to overlook a key component to it and other applications. Semantic technology helps users understand text, speech and relationships between data elements. Without it, other big data applications cannot be the success they seek. Tech Target brought this interesting topic to our attention in their article, “Semantic technology underpins conversational AI, other big data uses.”
Semantic tools are found in applications that parse speech, categorize questions and analyze sentiment. Uses include natural language processing, social networking, customer and healthcare analytics. You also can’t ignore artificial intelligence products like Amazon’s Alexa or IBM’s Watson.
Unfortunately, semantic applications often depend on complex deployments of data lakes that incorporate graph databases and another related technology — deep learning algorithms. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision and automatic speech recognition.
This family of technology works with and off one another to devise complex models and algorithms that enhance systems and lives.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
