基本信息
浏览量:0
职业迁徙
个人简介
RESEARCH EXPERIENCE
Explaining Similarity for SPARQL Queries Nanjing, China
Supervised by Prof. Meng Wang May 2020 to Nov. 2020
We aimed to provide explanations for typical SPARQL similarity measures. Given similarity scores of existing measures, we implemented explainable models based on four regression models to provide quantitative weights to different dimensional SPARQL features, i.e., we explained different kinds of SPARQL similarity computation models by presenting the weights of different dimensional SPARQL features captured by them.
I mainly worked on implementing SPARQL similarity computation models, as well as conducting rudimentary data analysis of similarity features in real-world query analysis, for example, the distribution and correlation of SPARQL similarity features. The analysis results are related to the choice of explainable models.
Explaining Similarity for SPARQL Queries Nanjing, China
Supervised by Prof. Meng Wang May 2020 to Nov. 2020
We aimed to provide explanations for typical SPARQL similarity measures. Given similarity scores of existing measures, we implemented explainable models based on four regression models to provide quantitative weights to different dimensional SPARQL features, i.e., we explained different kinds of SPARQL similarity computation models by presenting the weights of different dimensional SPARQL features captured by them.
I mainly worked on implementing SPARQL similarity computation models, as well as conducting rudimentary data analysis of similarity features in real-world query analysis, for example, the distribution and correlation of SPARQL similarity features. The analysis results are related to the choice of explainable models.
研究兴趣
论文共 6 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
AAAI 2024no. 9 (2024): 10748-10755
IJCAI 2023 (2023): 3383-3393
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn