SiWare: Contextual Understanding of Industrial Data for Situational Awareness

Anuradha Bhamidipaty; Elham Khabiri; Bhavna Agrawal; Yingjie Li

IJCAI 2023poster1 citations

SiWare: Contextual Understanding of Industrial Data for Situational Awareness

Anuradha Bhamidipaty, Elham Khabiri, Bhavna Agrawal, Yingjie Li

Abstract

SiWare is an AI-powered Knowledge Discovery system, that helps unlock new insights and accelerates data-driven decisions with contextualized Industrial data. SiWare links and fuses heterogeneous data sources with an industry semantic model leveraging multiple AI capabilities to provide system-wide visibility into operational characteristics. As part of this demo paper, we describe the requirements for such a system, and deployment aspects, and demonstrate the benefits in two industrial scenarios.

Data Mining: DM: Mining heterogenous dataData Mining: DM: Knowledge graphs and knowledge base completionNatural Language Processing: NLP: ApplicationsKnowledge Representation and Reasoning: KRR: Applications

BibTeX

@inproceedings{ijcai2023p829,
  title     = {SiWare: Contextual Understanding of Industrial Data for Situational Awareness},
  author    = {Bhamidipaty, Anuradha and Khabiri, Elham and Agrawal, Bhavna and Li, Yingjie},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {7115--7118},
  year      = {2023},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2023/829},
  url       = {https://doi.org/10.24963/ijcai.2023/829},
}