ML Anthology

High-Level Information Fusion with Bayesian Semantics

Paulo Cesar G. da Costa, Kathryn B. Laskey, Kuo-Chu Chang, Wei Sun, Cheol Young Park, Shou Matsumoto
UAI 2012 pp. 8-17
/uai/2012/dacosta2012uai-high/

Abstract

In an increasingly interconnected world information comes from various sources, usually with distinct, sometimes inconsistent semantics. Transforming raw data into high-level information fusion (HLIF) products, such as situation displays, automated decision support, and predictive analysis, relies heavily on human cognition. There is a clear lack of automated solutions for HLIF, making such systems prone to scalability issues. In this paper, we propose to address this issue with the use of highly expressive Bayesian models, which can provide a tighter link between information coming from low-level sources and the high-level information fusion systems, and allow for greater automation of the overall process. We illustrate our ideas with a naval HLIF system, and show the results of a preliminary set of experiments. 1

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Text

da Costa et al. "High-Level Information Fusion with Bayesian Semantics." Conference on Uncertainty in Artificial Intelligence, 2012.

Markdown

[da Costa et al. "High-Level Information Fusion with Bayesian Semantics." Conference on Uncertainty in Artificial Intelligence, 2012.](https://mlanthology.org/uai/2012/dacosta2012uai-high/)

BibTeX

@inproceedings{dacosta2012uai-high,
  title     = {{High-Level Information Fusion with Bayesian Semantics}},
  author    = {da Costa, Paulo Cesar G. and Laskey, Kathryn B. and Chang, Kuo-Chu and Sun, Wei and Park, Cheol Young and Matsumoto, Shou},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {2012},
  pages     = {8-17},
  url       = {https://mlanthology.org/uai/2012/dacosta2012uai-high/}
}