Six Core Aspects of Semantic AI


In this talk I will introduce and set a focus on a hybrid approach called ‘Semantic AI’, which makes use of machine learning like most contemporary AI efforts, but in combination with natural language processing and semantic technologies. We will discuss why enterprises need AI Governance, and how Semantic AI differs from other frameworks in this respect.
Semantic AI is an approach that comes with technical and organizational advantages. It’s more than ‘yet another machine learning algorithm’. It’s rather an AI strategy based on technical and organisational measures, which get implemented along the whole data lifecycle. Semantic AI provides a foundation for an enterprise-wide rollout of AI. In this talk I will also introduce the ‘Six core aspects of Semantic AI’, and we will discuss why this approach, as an integrated AI strategy, unfolds its effects on various levels.
In particular, I will give examples and more details about the six core aspects, which are:
  1. Hybrid approach: Semantic AI is the combination of methods derived from symbolic AI and statistical AI.
  2. Data Quality: Semantically enriched data serves as a basis for better data quality and provides more options for feature extraction.
  3. Data as a Service: Linked data based on W3C Semantic Web standards can serve as an enterprise-wide data platform and helps to provide training data for machine learning in a more cost-efficient way.
  4. Structured data meets text: Most machine learning algorithms work well either with text or with structured data, but those two types of data are rarely combined to serve as a whole.
  5. No black-box: In sharp contrast to AI technologies that ‘work like magic’, where only a few experts really understand the underlying techniques, Semantic AI seeks to provide an infrastructure to overcome information asymmetries between the developers of AI systems and other stakeholders.
  6. Towards self optimizing machines: Semantic AI is the next-generation Artificial Intelligence. Machine learning can help to extend knowledge graphs and, in return, knowledge graphs can help to improve ML algorithms (e.g., through ‘distant supervision’).
“Managing data in support of AI is not a one-off project, but an ongoing activity that should be formalized as part of your data management strategy” (Gartner 2018)


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