Hybrid Semantic Service Matchmaking Method Based on a Random Forest  

作　　者：Wei Jiang Junyu Lin Huiqiang Wang Shichen Zou 

机构地区：[1]the College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China [2]the Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China [3]Huawei Nanjing Research Institute,Nanjing 210012,China

出　　处：《Tsinghua Science and Technology》2020年第6期798-812,共15页清华大学学报（自然科学版（英文版）

基　　金：the National Natural Science Foundation of China(Nos.61872104 and 61502118);the National Science and Technology Major Project of China(No.2016ZX03001023-005);the Natural Science Foundation of Heilongjiang Province in China(No.F2016009)。

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.

关 键 词：Semantic Service Matchmaking(SSM) random forest logic-based service matchmaking FALSE-POSITIVE FALSE-NEGATIVE 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.1[自动化与计算机技术—控制科学与工程]