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Towards Semantic Reasoning in Knowledge Management Systems

Abstract : Modern applications of AI systems rely on their ability to acquire, represent and process expert knowledge for problem-solving and reasoning. Consequently, there has been significant interest in both industry and academia to establish advanced knowledge management (KM) systems, promoting the effective use of knowledge. In this paper, we examine the requirements and limitations of current commercial KM systems and propose a new approach to semantic reasoning supporting Big Data access, analytics, reporting and automation related tasks. We also provide comparative analysis of how state-of-the-art KM systems can benefit from semantics by illustrating examples from the life-sciences and industry. Lastly, we present results of our semantic-based analytics workflow implemented for Siemens power generation plants.
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Gulnar Mehdi, Sebastian Brandt, Mikhail Roshchin, Thomas Runkler. Towards Semantic Reasoning in Knowledge Management Systems. 4th IFIP International Workshop on Artificial Intelligence for Knowledge Management (AI4KM), Jul 2016, New York, NY, United States. pp.132-146, ⟨10.1007/978-3-319-92928-6_9⟩. ⟨hal-01950015⟩

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