Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

@article{Li2016EntropyWeightedIM,
  title={Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest},
  author={Lin Li and Xiaoyu Xing and Hui Xia and Xiaoying Huang},
  journal={Entropy},
  year={2016},
  volume={18},
  pages={45}
}
The crucial problem for integrating geospatial data is finding the corresponding objects (the counterpart) from different sources. Most current studies focus on object matching with individual attributes such as spatial, name, or other attributes, which avoids the difficulty of integrating those attributes, but at the cost of an ineffective matching. In this study, we propose an approach for matching instances by integrating heterogeneous attributes with the allocation of suitable attribute… CONTINUE READING

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