In one of their latest reports What Is Artificial Intelligence? Ignore the Hype; Here’s Where to Start by Erick Brethenoux (published 15 March 2022), Gartner states that, “Marketing hyperbole is increasing confusion around artificial intelligence (AI), resulting in many enterprises struggling to put a realistic value on an important source of innovation and differentiation. Business leadership tends to overestimate the impact of AI and underestimate its complexity — requiring data and analytics leaders to manage the business’s expectations, or risk costly project failure..”.
In an effort to help enterprises figure out what’s AI and what isn’t, Gartner proposes an AI Techniques Framework, which “consists of a set of computing engineering techniques that fall into seven principal categories”. .
The research further states that, “Using this framework, data and analytics leaders see through the hype, breaking the AI concept down into tangible pieces and removing the philosophical element from the AI discussion. They can then show how each of these techniques can solve real-world problems.”
Gartner includes Ontotext in the list of “Representative software vendors for knowledge representation”, which includes Knowledge Graphs.
Ontotext expertise in Natural Language Processing and Knowledge Representation, Learning & Search has helped some of the most knowledge intensive enterprises in various industries. Even though none of the lists of vendors claims to include all companies who have AI technology offerings or constitutes a Gartner endorsement of the organizations or their offerings, it’s still a sign of recognition for Ontotext technology and more than 20 years of hard work.
Ontotext portfolio also includes technology in few of the other sections from this framework. It’s our opinion that the company is well-positioned in two out of the three Established AI Techniques sections, which form most of the use cases in AI. Speaking of Computational Logic, Ontotext’s GraphDB engine is iconic for its capability to perform logical inference to large dynamic datasets, using a rule-based approach. Over the last few years, Ontotext has also extended the engine with few of the so-called Probabilistic Reasoning techniques, including machine learning and predictive modeling, based on word- and graph-embedding.
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For more information, contact Doug Kimball, Chief Marketing Officer at Ontotext