Too Much Information: Can AI Cope with Modern Knowledge Graphs?

  • Markus KrötzschEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11511)


Knowledge graphs play an important role in artificial intelligence (AI) applications – especially in personal assistants, question answering, and semantic search – and public knowledge bases like Wikidata are widely used in industry and research. However, modern AI includes many different techniques, including machine learning, data mining, natural language processing, which are often not able to use knowledge graphs in their full size and complexity. Feature engineering, sampling, and simplification are needed, and commonly achieved with custom preprocessing code. In this position paper, we argue that a more principled integrated approach to this task is possible using declarative methods from knowledge representation and reasoning. In particular, we suggest that modern rule-based systems are a promising platform for computing customised views on knowledge graphs, and for integrating the results of other AI methods back into the overall knowledge model.



This work is partly supported by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in project number 389792660 (TRR 248, Center for Perspicuous Systems), CRC 912 (Highly Adaptive Energy-Efficient Computing, HAEC), and Emmy Noether grant KR 4381/1-1.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.TU DresdenDresdenGermany

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