Name
Abstract | ||
|---|---|---|
Although there has been extensive research on developing seman- tic annotation tools recently, only few systems support automatic information extraction. In this paper, we propose a semantic anno- tation system named SARM, which has an automatic instance extraction module based on two machine learning techniques, Bayesian Classifier and Support Vector Machine. SARM has been tested to make a Korean Restaurant ontology evolve by automati- cally extracting instances from Web documents in Korean. The automatic instance extraction module can accelerate the annota- tion work which is very time-consuming and involves a lot of human labor. We describe the implementation of our system and also compare the performances of the two machine learning meth- ods we used. |
Year | Venue | Keywords |
|---|---|---|
2006 | SAAW@ISWC | bayesian classifier,ko- rean restaurant ontology,sarm,svm,information extraction |
Field | DocType | Volume |
Annotation,Naive Bayes classifier,Information retrieval,Semantic Web Stack,Computer science,Support vector machine,Semantic Web,Image retrieval,Information extraction,Social Semantic Web | Conference | 209 |
ISSN | Citations | PageRank |
16130073 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Hai-Tao | 1 | 142 | 24.39 |
| Bo-Yeong Kang | 2 | 152 | 16.94 |
| Koo Sang-Ok | 3 | 0 | 0.34 |
| Hee-Chul Choi | 4 | 32 | 2.86 |
| Kwangsub Kim | 5 | 23 | 1.49 |
| Hong-Gee Kim | 6 | 104 | 18.80 |