SigmaKB: multiple probabilistic knowledge base fusion

The interest in integrating web-scale knowledge bases (KBs) has intensified in the last several years. Research has focused on knowledge base completion between two KBs with complementary information, lacking any notion of uncertainty or method of handling conflicting information. We present SigmaKB, a knowledge base system that utilizes Consensus Maximization Fusion and user feedback to integrate and improve the query results of a total of 71 KBs. This paper presents the architecture and demonstration details.

Authors:

Miguel E. Rodríguez, Sean Goldberg, Daisy Zhe Wang

Bibtex:

@article{rodriguez2016sigmakb,
  title={SigmaKB: multiple probabilistic knowledge base fusion},
  author={Rodr{\'\i}guez, Miguel and Goldberg, Sean and Wang, Daisy Zhe},
  journal={Proceedings of the VLDB Endowment},
  volume={9},
  number={13},
  pages={1577--1580},
  year={2016},
  publisher={VLDB Endowment}
}

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