I am giving a talk (in French) at the 85th edition of the ACFAS congress, May 9. I will discuss the engineering aspects of doing machine learning. But more importantly, I will discuss how Semantic Web techniques, technologies and specifications can help solving the engineering problems and how they can be leveraged and integrated in a machine learning workflow. The focus of my talk is based on my work in the field of the semantic web in the last 15 years and my more recent work creating the KBpedia Knowledge Graph at Cognonto and how they influenced our work to develop different machine learning solutions to integrate data, to extend knowledge structure, to tag and disambiguate concepts and entities in corpuses of texts, etc. One thing we experienced is that most of the work involved in such project is not directly related to machine learning problems (or at least related to the usage of machine learning algorithms). And then I recently read a survey conducted by CrowdFlower in 2016 that support what we experienced.

I am giving a talk (in French) at the 85th edition of the ACFAS congress, May 9. I will discuss the engineering aspects of doing machine learning. But more importantly, I will discuss how Semantic Web techniques, technologies and specifications can help solving the engineering problems and how they can be leveraged and integrated in a machine learning workflow. The focus of my talk is based on my work in the field of the semantic web in the last 15 years and my more recent work creating the KBpedia Knowledge Graph at Cognonto and how they influenced our work to develop different machine learning solutions to integrate data, to extend knowledge structure, to tag and disambiguate concepts and entities in corpuses of texts, etc. One thing we experienced is that most of the work involved in such project is not directly related to machine learning problems (or at least related to the usage of machine learning algorithms). And then I recently read a survey conducted by CrowdFlower in 2016 that support what we experienced.

CORALVILLE, Iowa--(BUSINESS WIRE)--Cognonto, a new start-up in knowledge-based artificial intelligence (KBAI), announced today the dual release of its Cognonto Platform and KBpedia, a computable knowledge structure to automate much of the effort needed for machine learning. KBpedia leverages six large-scale knowledge bases -- Wikipedia, Wikidata, GeoNames, OpenCyc, DBpedia and UMBEL -- into a single structure expressly designed to support artificial intelligence (AI) within enterprises. "Many of the AI advances in recent years, such as question answering on smart phones or systems that beat human contestants in Jeopardy, are built around Web knowledge bases like Wikipedia," said Michael Bergman, a co-founder of Cognonto. "But these are one-off systems that only the largest tech firms or research outfits can afford," he said. "The idea behind Cognonto is to democratize this process such that any enterprise can afford to train their own machine learners or gain the advantages of knowledge-based artificial intelligence."