Embedding Uncertainty in Conceptual Graphs for Semantic Information Fusion

Author(s):  
Pawel Kowalski ◽  
Trevor Martin
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenghong Wang ◽  
Zhangjie Fu ◽  
Xingming Sun

Currently, searchable encryption becomes the focus topic with the emerging cloud computing paradigm. The existing research schemes are mainly semantic extensions of multiple keywords. However, the semantic information carried by the keywords is limited and does not respond well to the content of the document. And when the original scheme constructs the conceptual graph, it ignores the context information of the topic sentence, which leads to errors in the semantic extension. In this paper, we define and construct semantic search encryption scheme for context-based conceptual graph (ESSEC). We make contextual contact with the central key attributes in the topic sentence and extend its semantic information, so as to improve the accuracy of the retrieval and semantic relevance. Finally, experiments based on real data show that the scheme is effective and feasible.


2012 ◽  
Vol 263-266 ◽  
pp. 3274-3278
Author(s):  
Hui Ming Yu ◽  
Jian Zhong Guo ◽  
Yi Cheng ◽  
Qian Lou

Spatial data fusion is an important method of spatial data acquisition. The aim of multisource spatial data integration and fusion is to improve the information precision and information's utilization efficiency. Vector and raster are the two main spatial data structures. This article discusses vector data fusion from of data model fusion, semantic information fusion and coordinates unification, reviews the main methods of raster data fusion and discusses the key technologies of vector and raster data fusion, and proposes the future developments of spatial data fusion technique.


2021 ◽  
pp. 19-33
Author(s):  
Claire Laudy ◽  
Charlotte Jacobé de Naurois

2021 ◽  
pp. 394-402
Author(s):  
Haiyang Wang ◽  
Xin Song ◽  
Bin Zhou ◽  
Ye Wang ◽  
Liqun Gao ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document