Banerjee, AgnivaDalal, RakaMittal, SudipJoshi, Karuna Pande2018-10-182018-10-182017-06-25Agniva Banerjee, Raka Dalal, Sudip Mittal, Karuna Pande Joshi, Generating Digital Twin models using Knowledge Graphs for Industrial Production Lines, Workshop on Industrial Knowledge Graphs, co-located with the 9th International ACM Web Science Conference 2017,DOI: 10.1145/3091478.316238310.1145/3091478.3162383http://hdl.handle.net/11603/11592Workshop on Industrial Knowledge Graphs, co-located with the 9th International ACM Web Science Conference 2017Digital Twin models are computerized clones of physical assets that can be used for in-depth analysis. Industrial production lines tend to have multiple sensors to generate near real-time status information for production. Industrial Internet of Things datasets are difficult to analyze and infer valuable insights such as points of failure, estimated overhead. etc. In this paper we introduce a simple way of formalizing knowledge as digital twin models coming from sensors in industrial production lines. We present a way on to extract and infer knowledge from large scale production line data, and enhance manufacturing process management with reasoning capabilities, by introducing a semantic query mechanism. Our system primarily utilizes a graph-based query language equivalent to conjunctive queries and has been enriched with inference rules.6 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Digital TwinKnowledge GraphBig DataIndustrial Internet of ThingsSemantic WebUMBC Ebiquity Research GroupText