A Hybrid Approach of Knowledge Engineering and Machine Learning for the Discovery of Meaningful Insights from Customer Survey Data
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2021
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Master
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11 - Student thesis
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Hochschule für Wirtschaft FHNW
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Olten
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Abstract
The current hype of Artificial Intelligence (AI) technologies primarily refers to the success of Machine Learning (ML) and its sub-domain of deep learning. Nevertheless, the term AI also includes other knowledge-based areas such as Semantic Networks and Knowledge Representation and Reasoning (KRR). Machine Learning belongs to the branch of subsymbolic AI, which usually utilizes large and noisy datasets, learns and adapts from them to produce associative results without human intervention. On the other hand, symbolic AI is a reasoning oriented field providing high explainability of results and logical conclusions based on symbol-based methods, often in a human readable format, such as ontologies. Between both AI domains has been a long and unresolved debate since the 1950s, which is now nearing its end since the combination of symbolic and subsymbolic approaches is emerging as the most promising for the whole AI domain – resulting in so-called hybrid AI approaches. Since both branches have complementary strengths and weaknesses, cutting-edge systems arise from their intersections.
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English
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Strittmatter, M. (2021). A Hybrid Approach of Knowledge Engineering and Machine Learning for the Discovery of Meaningful Insights from Customer Survey Data [Hochschule für Wirtschaft FHNW]. https://irf.fhnw.ch/handle/11654/48610