Hybrid Semantic Service Matchmaking Method Based on a Random Forest

• 作者: Huiqiang Wang Junyu Lin Shichen Zou Wei Jiang
• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2020,（6）
• 页码范围: 798-812
• 页数: 15页
• 语种: 英文
• DOI: 10.26599/TST.2020.9010003

摘要

Semantic Service Matchmaking (SSM) can be leveraged for mining the most suitable service to accommodate a diversity of user demands.However,existing research on SSM mostly involves logical or non-logical matching,leading to unavoidable false-positive and false-negative problems.Combining different types of SSM methods is an effective way to improve this situation,but the adaptive combination of different service matching methods is still a difficult issue.To conquer this difficulty,a hybrid SSM method,which is based on a random forest and combines the advantages of existing SSM methods,is proposed in this paper.The result of each SSM method is treated as a multi-dimensional feature vector input for the random forest,converting the service matching into a two classification problem.Therefore,our method avoids the flaws found in manual threshold setting.Experimental results show that the proposed method achieves an outstanding performance.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	Wei Jiang		1	0	0.0	0.0
2	Junyu Lin		1	0	0.0	0.0
3	Huiqiang Wang		1	0	0.0	0.0
4	Shichen Zou		1	0	0.0	0.0