Effectively combining multiple (and complementary) sources of information is becoming one of the most promising paths for increased accuracy and more detailed analysis in numerous applications. Neuroscience, business analytics, military intelligence, and sociology are among the areas that could significantly benefit from properly processing diverse data sources. However, traditional methods for combining multiple sources of information are based on slow or impractical methods that rely either on vast amounts of manual processing or on suboptimal representations of data. We introduce an analytical framework that allows automatic and efficient processing of both hard (e.g., physics-based sensors) and soft (e.g., human-generated) information, leading to enhanced decision-making in multisource environments. This framework combines Natural Language Processing (NLP) methods for extracting information from soft data sources and the Dempster-Shafer (DS) Theory of Evidence as the common language for data representation and inference.
@inproceedings{premaratneetal12ahfe,
title={Credibility assessment and inference for fusion of hard and soft information},
author={K. Premaratne and R. Núñez and T. Wickramarathne and M. Murthi and M. Pravia and S. Kuebler and M. Scheutz},
year={2012},
booktitle={Proceedings of AHFE},
url={https://hrilab.tufts.edu/publications/premaratneetal12ahfe.pdf}
}