An Clustering-based Ontology Summarization Method with Structural and Semantic Information Integration

Author(s):  
Li Ran ◽  
Hu Xinbang
2013 ◽  
Vol 760-762 ◽  
pp. 1762-1766
Author(s):  
Qin Yi Ma ◽  
Ming Wei Wang ◽  
Mao Jun Zhou ◽  
Hui Hui Wang

The interoperation of various applications will need a representation that goes beyond the traditional geometry-based one, which is inadequate for capturing semantic information. The semantic file is proposed to support a tighter integration of CAD and FEA. The main idea of the approach is presented and key technologies are elaborated, including the creation of the FEA solution template, and the matching algorithm between semantic markup file and FEA template file t. Finally, the feasibility and effectiveness of the approach is empirically validated by a case study.


Author(s):  
Thomas Gannon ◽  
Stuart E. Madnick ◽  
Allen Moulton ◽  
Michael Siegel ◽  
Marwan Sabbouh ◽  
...  

2014 ◽  
Vol 533 ◽  
pp. 440-443
Author(s):  
Gang Huang ◽  
Xiu Ying Wu ◽  
Man Yuan

Due to information integration system is a need to focus on different periods independently designed data sources and a unified information system to provide their data to the end user, so it will inevitably encounter data changes over time to bring the knowledge of information contained, the concept will be certain changes in circumstances occur. This paper analyzes the semantic-oriented information integration systems and solutions proposed to consider the full range of semantic information integration problems at different stages of the primary purposes of information integration systems.


2011 ◽  
Vol 225-226 ◽  
pp. 827-830
Author(s):  
Ai Wen Jiang ◽  
Gao Rong Zeng

Video text provides important semantic information in video content analysis. However, video text with complex background has a poor recognition performance for OCR. Most of the previous approaches to extracting overlay text from videos are based on traditional binarization and give little attention on multi-information integration, especially fusing the background information. This paper presents an effective method to precisely extract characters from videos to enable it for OCR with a good recognition performance. The proposed method combines multi-information together including background information, edge information, and character’s spatial information. Experimental results show that it is robust to complex background and various text appearances.


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